subject:(Reinforcement Learning)

Oregon State University

1. Mehta, Neville. Hierarchical structure discovery and transfer in sequential decision problems.

Degree: PhD, Computer Science, 2011, Oregon State University

URL: http://hdl.handle.net/1957/25199

► Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or… (more)

▼ Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or near-optimal decision-making, and transfer it to related problems instead of solving every problem in isolation. This dissertation makes three contributions toward this goal. The first contribution is the introduction of two frameworks for the transfer of hierarchical structure in sequential decision problems. The MASH framework facilitates transfer among multiple agents coordinating within a domain. The VRHRL framework allows an agent to transfer its knowledge across a family of domains that share the same transition dynamics but have differing reward dynamics. Both MASH and VRHRL are validated empirically in large domains and the results demonstrate significant speedup in the solutions due to transfer. The second contribution is a new approach to the discovery of hierarchical structure in sequential decision problems. HI-MAT leverages action models to analyze the relevant dependencies in a hierarchically-generated trajectory and it discovers hierarchical structure that transfers to all problems whose actions share the same relevant dependencies as the single source problem. HierGen advances HI-MAT by learning simple action models, leveraging these models to analyze non-hierarchically-generated trajectories from multiple source problems in a robust causal fashion, and discovering hierarchical structure that transfers to all problems whose actions share the same causal dependencies as those in the source problems. Empirical evaluations in multiple domains demonstrate that the discovered hierarchical structures are comparable to manually-designed structures in quality and performance. Action models are essential to hierarchical structure discovery and other aspects of intelligent behavior. The third contribution of this dissertation is the introduction of two general frameworks for learning action models in sequential decision problems. In the MBP framework, learning is user-driven; in the PLEX framework, the learner generates its own problems. The frameworks are formally analyzed and reduced to concept learning with one-sided error. A general action-modeling language is shown to be efficiently learnable in both frameworks. Advisors/Committee Members: Tadepalli, Prasad (advisor), Dietterich, Thomas (committee member).

Subjects/Keywords: hierarchical reinforcement learning; Reinforcement learning

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APA (6^th Edition):

Mehta, N. (2011). Hierarchical structure discovery and transfer in sequential decision problems. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/25199

Chicago Manual of Style (16^th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Doctoral Dissertation, Oregon State University. Accessed February 17, 2020. http://hdl.handle.net/1957/25199.

MLA Handbook (7^th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Web. 17 Feb 2020.

Vancouver:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/1957/25199.

Council of Science Editors:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/25199

Oregon State University

2. Proper, Scott. Scaling multiagent reinforcement learning.

Degree: PhD, Computer Science, 2009, Oregon State University

URL: http://hdl.handle.net/1957/13662

► Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent… (more)

▼ Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent domains are particularly susceptible to these problems. This thesis describes ways to mitigate these curses in several different multiagent domains, including real-time delivery of products using multiple vehicles with stochastic demands, a multiagent predator-prey domain, and a domain based on a real-time strategy game. To mitigate the problem of state-space explosion, this thesis present several approaches that mitigate each of these curses. "Tabular linear functions" (TLFs) are introduced that generalize tile-coding and linear value functions and allow learning of complex nonlinear functions in high-dimensional state-spaces. It is also shown how to adapt TLFs to relational domains, creating a "lifted" version called relational templates. To mitigate the problem of action-space explosion, the replacement of complete joint action space search with a form of hill climbing is described. To mitigate the problem of outcome space explosion, a more efficient calculation of the expected value of the next state is shown, and two real-time dynamic programming algorithms based on afterstates, ASH-learning and ATR-learning, are introduced. Lastly, two approaches that scale by treating a multiagent domain as being formed of several coordinating agents are presented. "Multiagent H-learning" and "Multiagent ASH-learning" are described, where coordination is achieved through a method called "serial coordination". This technique has the benefit of addressing each of the three curses of dimensionality simultaneously by reducing the space of states and actions each local agent must consider. The second approach to multiagent coordination presented is "assignment-based decomposition", which divides the action selection step into an assignment phase and a primitive action selection step. Like the multiagent approach, assignment-based decomposition addresses all three curses of dimensionality simultaneously by reducing the space of states and actions each group of agents must consider. This method is capable of much more sophisticated coordination. Experimental results are presented which show successful application of all methods described. These results demonstrate that the scaling techniques described in this thesis can greatly mitigate the three curses of dimensionality and allow solutions for multiagent domains to scale to large numbers of agents, and complex state and outcome spaces. Advisors/Committee Members: Tadepalli, Prasad (advisor), Dietterich, Thomas (committee member).

Subjects/Keywords: Reinforcement learning; Reinforcement learning

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APA (6^th Edition):

Proper, S. (2009). Scaling multiagent reinforcement learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/13662

Chicago Manual of Style (16^th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed February 17, 2020. http://hdl.handle.net/1957/13662.

MLA Handbook (7^th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Web. 17 Feb 2020.

Vancouver:

Proper S. Scaling multiagent reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/1957/13662.

Council of Science Editors:

Proper S. Scaling multiagent reinforcement learning. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/13662

University of Illinois – Urbana-Champaign

3. Potok, Matthew. Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study.

Degree: MS, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/102518

► Reinforcement learning (RL) is a general method for agents to learn optimal control policies through exploration and experience. Due to its generality, RL can generate… (more)

Subjects/Keywords: Reinforcement Learning

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APA (6^th Edition):

Potok, M. (2018). Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102518

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Potok, Matthew. “Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed February 17, 2020. http://hdl.handle.net/2142/102518.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Potok, Matthew. “Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study.” 2018. Web. 17 Feb 2020.

Vancouver:

Potok M. Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/2142/102518.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Potok M. Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102518

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

4. Lauer, Christopher Joseph. Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming.

Degree: PhD, 2017, Oregon State University

URL: http://hdl.handle.net/1957/61678

► Forest management in the face of fire risk is a challenging problem because fire spreads across a landscape and because its occurrence is unpredictable. Additionally,… (more)

▼ Forest management in the face of fire risk is a challenging problem because fire spreads across a landscape and because its occurrence is unpredictable. Additionally, management must be adjusted over time as unpredictable fire events are realized. Land managers have some control over the vegetation conditions that facilitate fire spread, and they can engage in activity that reduces fire arrival probability and decreases the likelihood of damage. This management activity will have consequences for fire risk in multiple locations on a landscape over a long time-horizon. The first manuscript in this dissertation demonstrates a method for incorporating spatial information and interactions into a dynamic model for management decisions. Approximate dynamic programming is applied to determine the optimal timing and location of fuel treatments and timber harvests for a fire-threatened landscape. This method allows a value function and an optimal policy to be found for a problem with high dimension state and action spaces. Larger net present values can be achieved using policies that explicitly consider evolving spatial interactions created by fire spread, compared to policies that are optimal in a non-spatial or non-dynamic setting. The second manuscript explores the interaction of multiple landowners on a single landscape. Fuel conditions that allow fire to cross property boundaries creates externalities, and must be accounted for when making management decisions. In the second manuscript, the problem of determining optimal management is formulated as a dynamic game where each agent affects the fire risk for other agents on the landscape. Value functions and optimal policies that account for fire-spread externalities are determined for each agent. This method is applied to analyze the effect of ownership fragmentation on landowner welfare and ecological outcomes. The third manuscript explores policies designed to make landowners accountable for externalities generated by their management. Damage caused by spreading fires has prompted the creation of laws such as liability and negligence standards for landowners. Large damaging fires that resulted in expensive litigation have prompted changes to wildfire liability laws in several states. These regulations change the incentives that managers face. In this manuscript, the multi-agent framework developed in the second part of this dissertation is expanded and applied to analyze the effect of different types of liability regulations. Advisors/Committee Members: Montgomery, Claire A. (advisor), Dietterich, Thomas G. (committee member).

Subjects/Keywords: reinforcement learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lauer, C. J. (2017). Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61678

Chicago Manual of Style (16^th Edition):

Lauer, Christopher Joseph. “Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming.” 2017. Doctoral Dissertation, Oregon State University. Accessed February 17, 2020. http://hdl.handle.net/1957/61678.

MLA Handbook (7^th Edition):

Lauer, Christopher Joseph. “Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming.” 2017. Web. 17 Feb 2020.

Vancouver:

Lauer CJ. Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/1957/61678.

Council of Science Editors:

Lauer CJ. Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61678

Delft University of Technology

5. Van Diepen, M.D.M. Avoiding failure states during reinforcement learning:.

Degree: 2011, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6

► The Delft Biorobotics Laboratory develops bipedal humanoid robots. One of these robots, called LEO, is designed to learn to walk using reinforcement learning. During learning,… (more)

▼ The Delft Biorobotics Laboratory develops bipedal humanoid robots. One of these robots, called LEO, is designed to learn to walk using reinforcement learning. During learning, LEO will make mistakes and fall. These mistakes can cause serious dam- age to the system but are an integral part of the learning process. A likely solution is punishing the robot more severely for falling. However, punishing the robot too rigorously can lead to a robot that is too cautious to make a step. In this research, three methods were tested that reduce the amount of falls during learning, without restricting the possible solutions or increasing the learning time. We introduce Threshold Restricted Learning (TRL), a new method for action se- lection that, during exploration, makes the probability an action is chosen dependent on the expected reward for taking that action. Actions with expected rewards below the set threshold have a signifcant reduced probability of being chosen. The concept of TRL developed from the desire to optimize the use of a pre-learned solution. Hence, TRL was tested after pre-learning in simulation. The largest reduction in the amount of falls achieved by TRL in this research was 50% TRL did not increase the learning time. Without pre-learning the largest reduction was 7%. Softmax action selection is a well known but underused selection method. Combined with pre-learning it was able to reduce the amount of falls during learning with approximately 80% and did not increase the learning time either. Without pre-learning, Softmax could still achieve a reduction of approximately 20%. Sarsa2Q, another new method, stores expected rewards at different levels op generalization. The level with little generalization is used to store the expected total positive reward, while the level with more generalization is used to store the expected total negative reward. Sarsa2Q learned to avoid the failure states for the inverted pendulum problem faster than using a single level of generalization. This method does not use pre-learning. The highest achieved reduction in the amount of falls was approximately 20%. Sarsa2Q can have a broader use than just avoiding failure states. Rather than only using the coarse generalization for negative rewards, it can be used for all rewards that need less precision. Advisors/Committee Members: Jonker, P.P., Schuitema, E..

Subjects/Keywords: reinforcement learning

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APA (6^th Edition):

Van Diepen, M. D. M. (2011). Avoiding failure states during reinforcement learning:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6

Chicago Manual of Style (16^th Edition):

Van Diepen, M D M. “Avoiding failure states during reinforcement learning:.” 2011. Masters Thesis, Delft University of Technology. Accessed February 17, 2020. http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6.

MLA Handbook (7^th Edition):

Van Diepen, M D M. “Avoiding failure states during reinforcement learning:.” 2011. Web. 17 Feb 2020.

Vancouver:

Van Diepen MDM. Avoiding failure states during reinforcement learning:. [Internet] [Masters thesis]. Delft University of Technology; 2011. [cited 2020 Feb 17]. Available from: http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6.

Council of Science Editors:

Van Diepen MDM. Avoiding failure states during reinforcement learning:. [Masters Thesis]. Delft University of Technology; 2011. Available from: http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6

Delft University of Technology

6. Van Rooijen, J.C. Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:.

Degree: 2012, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38

► The reinforcement learning (RL) framework enables to construct controllers that try to find find an optimal control strategy in an unknown environment by trial-and-error. After… (more)

▼ The reinforcement learning (RL) framework enables to construct controllers that try to find find an optimal control strategy in an unknown environment by trial-and-error. After selecting a control action, the controller receives a numerical reward. The reward signal is based on the current state of the environment and the applied control action. The controller aims to maximize the cumulative reward, known as the return. In this thesis actor-critic and criticonly RL algorithms are considered. Actor-critic algorithms consist of an element that selects the actions (the actor) and an element that learns the expectation of the return (the critic). This expectation is captured in a value function. The critic is used to improve the control policy of the actor. Critic-only algorithms select the action by direct optimization over a value function. Before a RL algorithm can be applied to a control problem, a number of learning parameters need to be set. The optimal values of some of these parameters are highly problem dependent. It is not straightforward how these parameters should be chosen and often these are often determined by trying a large set of parameters. The main focus of thesis is to devise an action selection method that is able to select continuous actions without problem dependent parameters. Two approaches are taken: first, it is investigated if Levenberg Marquardt (LM), a popular optimization method, can be used to determine the actor update step. Second, an action selection method is treated that lacks an explicit actor, called Value-Gradient Based Policy (VGBP). The LM algorithm uses the gradient and the Hessian to compute the update step. Therefore the policy gradient and Hessian need to be found. A novel actor-critic method has been devised, called Vanilla Actor-Critic (VAC), that efficiently learns the policy gradient. On the inverted pendulum swing-up task this algorithm outperformed Natural Actor-Critic (NAC). A number of different approaches have been taken to approximate the policy Hessian, but none delivered a proper Hessian estimate. Therefore, no LM actor update method was created. In VGBP the action is found by optimization of the right hand side of the Bellman equation. VGBP uses the provided reward function and a process model for this optimization. The process model is learned online using local linear regression (LLR). Due to the efficient use of information VGBP shows fast learning on the pendulum and a 2-DOF robotic arm. Advisors/Committee Members: Grondman, I., Babuska, R..

Subjects/Keywords: reinforcement learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Van Rooijen, J. C. (2012). Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38

Chicago Manual of Style (16^th Edition):

Van Rooijen, J C. “Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:.” 2012. Masters Thesis, Delft University of Technology. Accessed February 17, 2020. http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38.

MLA Handbook (7^th Edition):

Van Rooijen, J C. “Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:.” 2012. Web. 17 Feb 2020.

Vancouver:

Van Rooijen JC. Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:. [Internet] [Masters thesis]. Delft University of Technology; 2012. [cited 2020 Feb 17]. Available from: http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38.

Council of Science Editors:

Van Rooijen JC. Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:. [Masters Thesis]. Delft University of Technology; 2012. Available from: http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38

7. Frank, Mikhail Alexander. Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning.

Degree: 2014, Università della Svizzera italiana

URL: http://doc.rero.ch/record/234387

► The next generation of intelligent robots will need to be able to plan reaches. Not just ballistic point to point reaches, but reaches around things… (more)

▼ The next generation of intelligent robots will need to be able to plan reaches. Not just ballistic point to point reaches, but reaches around things such as the edge of a table, a nearby human, or any other known object in the robot’s workspace. Planning reaches may seem easy to us humans, because we do it so intuitively, but it has proven to be a challenging problem, which continues to limit the versatility of what robots can do today. In this document, I propose a novel intrinsically motivated RL system that draws on both Path/Motion Planning and Reactive Control. Through Reinforcement Learning, it tightly integrates these two previously disparate approaches to robotics. The RL system is evaluated on a task, which is as yet unsolved by roboticists in practice. That is to put the palm of the iCub humanoid robot on arbitrary target objects in its workspace, start- ing from arbitrary initial configurations. Such motions can be generated by planning, or searching the configuration space, but this typically results in some kind of trajectory, which must then be tracked by a separate controller, and such an approach offers a brit- tle runtime solution because it is inflexible. Purely reactive systems are robust to many problems that render a planned trajectory infeasible, but lacking the capacity to search, they tend to get stuck behind constraints, and therefore do not replace motion planners. The planner/controller proposed here is novel in that it deliberately plans reaches without the need to track trajectories. Instead, reaches are composed of sequences of reactive motion primitives, implemented by my Modular Behavioral Environment (MoBeE), which provides (fictitious) force control with reactive collision avoidance by way of a realtime kinematic/geometric model of the robot and its workspace. Thus, to the best of my knowledge, mine is the first reach planning approach to simultaneously offer the best of both the Path/Motion Planning and Reactive Control approaches. By controlling the real, physical robot directly, and feeling the influence of the con- straints imposed by MoBeE, the proposed system learns a stochastic model of the iCub’s configuration space. Then, the model is exploited as a multiple query path planner to find sensible pre-reach poses, from which to initiate reaching actions. Experiments show that the system can autonomously find practical reaches to target objects in workspace and offers excellent robustness to changes in the workspace configuration as well as noise in the robot’s sensory-motor apparatus. Advisors/Committee Members: Schmidhuber, Jürgen (Dir.), Alexander (Codir.).

Subjects/Keywords: Reinforcement learning

11 more images…

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APA (6^th Edition):

Frank, M. A. (2014). Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning. (Thesis). Università della Svizzera italiana. Retrieved from http://doc.rero.ch/record/234387

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Frank, Mikhail Alexander. “Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning.” 2014. Thesis, Università della Svizzera italiana. Accessed February 17, 2020. http://doc.rero.ch/record/234387.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Frank, Mikhail Alexander. “Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning.” 2014. Web. 17 Feb 2020.

Vancouver:

Frank MA. Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning. [Internet] [Thesis]. Università della Svizzera italiana; 2014. [cited 2020 Feb 17]. Available from: http://doc.rero.ch/record/234387.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Frank MA. Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning. [Thesis]. Università della Svizzera italiana; 2014. Available from: http://doc.rero.ch/record/234387

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Aberdeen

8. Alexander, John W. Transfer in reinforcement learning.

Degree: PhD, 2015, University of Aberdeen

URL: http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561

► The problem of developing skill repertoires autonomously in robotics and artificial intelligence is becoming ever more pressing. Currently, the issues of how to apply prior… (more)

▼ The problem of developing skill repertoires autonomously in robotics and artificial intelligence is becoming ever more pressing. Currently, the issues of how to apply prior knowledge to new situations and which knowledge to apply have not been sufficiently studied. We present a transfer setting where a reinforcement learning agent faces multiple problem solving tasks drawn from an unknown generative process, where each task has similar dynamics. The task dynamics are changed by varying in the transition function between states. The tasks are presented sequentially with the latest task presented considered as the target for transfer. We describe two approaches to solving this problem. Firstly we present an algorithm for transfer of the function encoding the stateaction value, defined as value function transfer. This algorithm uses the value function of a source policy to initialise the policy of a target task. We varied the type of basis the algorithm used to approximate the value function. Empirical results in several well known domains showed that the learners benefited from the transfer in the majority of cases. Results also showed that the Radial basis performed better in general than the Fourier. However contrary to expectation the Fourier basis benefited most from the transfer. Secondly, we present an algorithm for learning an informative prior which encodes beliefs about the underlying dynamics shared across all tasks. We call this agent the Informative Prior agent (IP). The prior is learnt though experience and captures the commonalities in the transition dynamics of the domain and allows for a quantification of the agent's uncertainty about these. By using a sparse distribution of the uncertainty in the dynamics as a prior, the IP agent can successfully learn a model of 1) the set of feasible transitions rather than the set of possible transitions, and 2) the likelihood of each of the feasible transitions. Analysis focusing on the accuracy of the learned model showed that IP had a very good accuracy bound, which is expressible in terms of only the permissible error and the diffusion, a factor that describes the concentration of the prior mass around the truth, and which decreases as the number of tasks experienced grows. The empirical evaluation of IP showed that an agent which uses the informative prior outperforms several existing Bayesian reinforcement learning algorithms on tasks with shared structure in a domain where multiple related tasks were presented only once to the learners. IP is a step towards the autonomous acquisition of behaviours in artificial intelligence. IP also provides a contribution towards the analysis of exploration and exploitation in the transfer paradigm.

Subjects/Keywords: Reinforcement learning; Learning

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APA (6^th Edition):

Alexander, J. W. (2015). Transfer in reinforcement learning. (Doctoral Dissertation). University of Aberdeen. Retrieved from http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561

Chicago Manual of Style (16^th Edition):

Alexander, John W. “Transfer in reinforcement learning.” 2015. Doctoral Dissertation, University of Aberdeen. Accessed February 17, 2020. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561.

MLA Handbook (7^th Edition):

Alexander, John W. “Transfer in reinforcement learning.” 2015. Web. 17 Feb 2020.

Vancouver:

Alexander JW. Transfer in reinforcement learning. [Internet] [Doctoral dissertation]. University of Aberdeen; 2015. [cited 2020 Feb 17]. Available from: http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561.

Council of Science Editors:

Alexander JW. Transfer in reinforcement learning. [Doctoral Dissertation]. University of Aberdeen; 2015. Available from: http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561

University of New South Wales

9. Ismail, Hafsa. A neural network framework for combining different task types and motivations in motivated reinforcement learning.

Degree: Engineering & Information Technology, 2014, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/53975 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12686/SOURCE02?view=true

► Combining different motivation models for different task types within artificial agents has the potential to produce agents capable of a greater range of behaviours in… (more)

▼ Combining different motivation models for different task types within artificial agents has the potential to produce agents capable of a greater range of behaviours in different situations. This thesis will explore the ability to produce agents that can identify different types of tasks online then learn solutions for these tasks while driven by one or more motivations in isolation or combination to produce different agent behaviour that will focus on different task types. The main contributions of this research are: Definitions for four task types for use in motivated reinforcement learning agents; a multi-layer neural network model for combining different motivations for different task types; a study of agent ability to identify different types of tasks in a simulated game scenario. A review of existing computational models of motivation and the layered approaches to the design of goal generation and intrinsically motivated learning agents inspire the multi-layer neural network framework presented and examined experimentally in this thesis. The first set of experiments examines topological and non-topological adaptive resonance theory networks that are used to classify sensed states and identify different types of tasks. Different distance metrics and weight update rules are used to determine the impact of the network structure on identified task types. The most promising network structure, distance metric and update rule is then chosen to implement the task identification process in conjunction with motivated reinforcement learning. In the second set of experiments, three motivation models, novelty, interest and competence, are examined separately in conjunction with our task identification algorithm. Established metrics and statistical methods are used to quantify learning behaviour and the complexity and variety of repetitive behavioural structures. Finally, we examine agent behaviour when the agent can identify more than one type of task with a single motive model that drives the learning process. Parallel and series task types combination modes are explored. Results show that a series approach to changing the type of task the agent can identify is most effective for producing motivated reinforcement learning agents that can both learn effectively and exhibit quantifiable changes in their characteristic learning behaviour over time. Advisors/Committee Members: Merrick, Kathryn, Engineering & Information Technology, Australian Defence Force Academy, UNSW, Barlow, Michael, Engineering & Information Technology, Australian Defence Force Academy, UNSW.

Subjects/Keywords: Motivated Reinforcement Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ismail, H. (2014). A neural network framework for combining different task types and motivations in motivated reinforcement learning. (Masters Thesis). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53975 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12686/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Ismail, Hafsa. “A neural network framework for combining different task types and motivations in motivated reinforcement learning.” 2014. Masters Thesis, University of New South Wales. Accessed February 17, 2020. http://handle.unsw.edu.au/1959.4/53975 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12686/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Ismail, Hafsa. “A neural network framework for combining different task types and motivations in motivated reinforcement learning.” 2014. Web. 17 Feb 2020.

Vancouver:

Ismail H. A neural network framework for combining different task types and motivations in motivated reinforcement learning. [Internet] [Masters thesis]. University of New South Wales; 2014. [cited 2020 Feb 17]. Available from: http://handle.unsw.edu.au/1959.4/53975 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12686/SOURCE02?view=true.

Council of Science Editors:

Ismail H. A neural network framework for combining different task types and motivations in motivated reinforcement learning. [Masters Thesis]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53975 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12686/SOURCE02?view=true

University of St. Andrews

10. Aquili, Luca. Refinement of biologically inspired models of reinforcement learning .

Degree: 2010, University of St. Andrews

URL: http://hdl.handle.net/10023/886

► Reinforcement learning occurs when organisms adapt the propensities of given behaviours on the basis of associations with reward and punishment. Currently, reinforcement learning models have… (more)

▼ Reinforcement learning occurs when organisms adapt the propensities of given behaviours on the basis of associations with reward and punishment. Currently, reinforcement learning models have been validated in minimalist environments in which only 1-2 environmental stimuli are present as possible predictors of reward. The exception to this is two studies in which the responses of the dopamine system to configurations of multiple stimuli were investigated, however, in both cases the stimuli were presented simultaneously rather than in a sequence. Therefore, we set out to understand how current models of reinforcement learning would respond under more complex conditions in which sequences of events are predictors of reward. In the two experimental chapters of this thesis, we attempted to understand whether midbrain dopaminergic neurons would respond to occasion setters (Chapter 3), and to the overexpectation effect (Chapter 4). In addition, we ran simulations of the behavioural paradigms using temporal difference models of reinforcement learning (Chapter 2) and compared the predictions of the model with the behavioural and neurophysiological data. In Chapter 3, by performing single-neuron recording from VTA and SNpc dopaminergic cells, we demonstrated that our population of neurons were most responsive to the latest predictor of reward, the conditioned stimulus (CS) and not the earliest, the occasion setter (the OS). This is in stark contrast with the predictions of the model (Chapter 2), where the greatest response is seen at the OS onset. We also showed at a neural level that there was only a weak enhancement of the response to the discriminative stimulus (SD) when this was preceded by the OS. On the other hand, at a behavioural level, bar pressing was greatest when the SD was preceded by the OS, demonstrating that rats could use the information provided by the OS, but that dopamine was not controlling the conditioned response. In Chapter 4, our population of dopaminergic neurons showed that they would preferentially respond to only one of the two conditioned stimuli (CSA, CSB) in the overexpectation paradigm. The predictions of the model (Chapter 2) suggested that when the two stimuli would be presented in compound, there would be an inhibitory response if the reward magnitude was kept constant and an excitatory response if the reward magnitude was doubled. The lack of neural firing to one of the two conditioned stimuli, however, does not make for easy interpretation of the data. Perhaps, one of the conditioned stimuli acted as if it were overshadowing the other, resulting in no response to the second CS. Interestingly, at a behavioural level, we did not see increased licking frequency to the compound stimuli presentation, a result that is somewhat at odds with the previous literature. Overall, the results of our experimental chapters suggest that the role that midbrain dopaminergic neurons play in reinforcement learning is more complex than that envisaged by previous… Advisors/Committee Members: Bowman, Eric MacDonald (advisor).

Subjects/Keywords: Dopaminergic; Reinforcement learning

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APA (6^th Edition):

Aquili, L. (2010). Refinement of biologically inspired models of reinforcement learning . (Thesis). University of St. Andrews. Retrieved from http://hdl.handle.net/10023/886

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Aquili, Luca. “Refinement of biologically inspired models of reinforcement learning .” 2010. Thesis, University of St. Andrews. Accessed February 17, 2020. http://hdl.handle.net/10023/886.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Aquili, Luca. “Refinement of biologically inspired models of reinforcement learning .” 2010. Web. 17 Feb 2020.

Vancouver:

Aquili L. Refinement of biologically inspired models of reinforcement learning . [Internet] [Thesis]. University of St. Andrews; 2010. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10023/886.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Aquili L. Refinement of biologically inspired models of reinforcement learning . [Thesis]. University of St. Andrews; 2010. Available from: http://hdl.handle.net/10023/886

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

11. Wilson, Aaron (Aaron Creighton). Bayesian methods for knowledge transfer and policy search in reinforcement learning.

Degree: PhD, Computer Science, 2012, Oregon State University

URL: http://hdl.handle.net/1957/34550

► How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action… (more)

▼ How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented knowledge when selecting actions. Our first contribution introduces the multi-task Reinforcement Learning setting in which an agent solves a sequence of tasks. An agent equipped with knowledge of the relationship between tasks can transfer knowledge between them. We propose the transfer of two distinct types of knowledge: knowledge of domain models and knowledge of policies. To represent the transferable knowledge, we propose hierarchical Bayesian priors on domain models and policies respectively. To transfer domain model knowledge, we introduce a new algorithm for model-based Bayesian Reinforcement Learning in the multi-task setting which exploits the learned hierarchical Bayesian model to improve exploration in related tasks. To transfer policy knowledge, we introduce a new policy search algorithm that accepts a policy prior as input and uses the prior to bias policy search. A specific implementation of this algorithm is developed that accepts a hierarchical policy prior. The algorithm learns the hierarchical structure and reuses components of the structure in related tasks. Our second contribution addresses the basic problem of generalizing knowledge gained from previously-executed policies. Bayesian Optimization is a method of exploiting a prior model of an objective function to quickly identify the point maximizing the modeled objective. Successful use of Bayesian Optimization in Reinforcement Learning requires a model relating policies and their performance. Given such a model, Bayesian Optimization can be applied to search for an optimal policy. Early work using Bayesian Optimization in the Reinforcement Learning setting ignored the sequential nature of the underlying decision problem. The work presented in this thesis explicitly addresses this problem. We construct new Bayesian models that take advantage of sequence information to better generalize knowledge across policies. We empirically evaluate the value of this approach in a variety of Reinforcement Learning benchmark problems. Experiments show that our method significantly reduces the amount of exploration required to identify the optimal policy. Our final contribution is a new framework for learning parametric policies from queries presented to an expert. In many domains it is difficult to provide expert demonstrations of desired policies. However, it may still be a simple matter for an expert to identify good and bad performance. To take advantage of this limited expert knowledge, our agent presents experts with pairs of demonstrations and asks which of the demonstrations best represents a latent target behavior. The goal is to use a small number of… Advisors/Committee Members: Tadepalli, Prasad (advisor), Tom, Dietterich (committee member).

Subjects/Keywords: Machine Learning; Reinforcement learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wilson, A. (. C. (2012). Bayesian methods for knowledge transfer and policy search in reinforcement learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/34550

Chicago Manual of Style (16^th Edition):

Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Doctoral Dissertation, Oregon State University. Accessed February 17, 2020. http://hdl.handle.net/1957/34550.

MLA Handbook (7^th Edition):

Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Web. 17 Feb 2020.

Vancouver:

Wilson A(C. Bayesian methods for knowledge transfer and policy search in reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2012. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/1957/34550.

Council of Science Editors:

Wilson A(C. Bayesian methods for knowledge transfer and policy search in reinforcement learning. [Doctoral Dissertation]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/34550

Rutgers University

12. Marivate, Vukosi N. Improved empirical methods in reinforcement-learning evaluation.

Degree: PhD, Computer Science, 2015, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/46389/

►

The central question addressed in this research is ”can we define evaluation methodologies that encourage reinforcement-learning (RL) algorithms to work effectively with real-life data?” First,… (more)

Subjects/Keywords: Reinforcement learning; Machine learning; Algorithms

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Marivate, V. N. (2015). Improved empirical methods in reinforcement-learning evaluation. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/46389/

Chicago Manual of Style (16^th Edition):

Marivate, Vukosi N. “Improved empirical methods in reinforcement-learning evaluation.” 2015. Doctoral Dissertation, Rutgers University. Accessed February 17, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/46389/.

MLA Handbook (7^th Edition):

Marivate, Vukosi N. “Improved empirical methods in reinforcement-learning evaluation.” 2015. Web. 17 Feb 2020.

Vancouver:

Marivate VN. Improved empirical methods in reinforcement-learning evaluation. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Feb 17]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46389/.

Council of Science Editors:

Marivate VN. Improved empirical methods in reinforcement-learning evaluation. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46389/

University of Waterloo

13. Gaurav, Ashish. Safety-Oriented Stability Biases for Continual Learning.

Degree: 2020, University of Waterloo

URL: http://hdl.handle.net/10012/15579

► Continual learning is often confounded by “catastrophic forgetting” that prevents neural networks from learning tasks sequentially. In the case of real world classification systems that… (more)

Subjects/Keywords: deep learning; continual learning; classification; reinforcement learning

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APA (6^th Edition):

Gaurav, A. (2020). Safety-Oriented Stability Biases for Continual Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15579

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Gaurav, Ashish. “Safety-Oriented Stability Biases for Continual Learning.” 2020. Thesis, University of Waterloo. Accessed February 17, 2020. http://hdl.handle.net/10012/15579.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Gaurav, Ashish. “Safety-Oriented Stability Biases for Continual Learning.” 2020. Web. 17 Feb 2020.

Vancouver:

Gaurav A. Safety-Oriented Stability Biases for Continual Learning. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10012/15579.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gaurav A. Safety-Oriented Stability Biases for Continual Learning. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15579

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

14. Lin, Kun-da. Deep Reinforcement Learning with a Gating Network.

Degree: Master, Electrical Engineering, 2017, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536

► Reinforcement Learning (RL) is a good way to train the robot since it doesn't need an exact model of the environment. All need is to… (more)

▼ Reinforcement Learning (RL) is a good way to train the robot since it doesn't need an exact model of the environment. All need is to let a learning agent interact with the en-vironment by an appropriate reward function, which is associated with the goal of the task that the agent is expected to accomplish. Unfortunately, itâs hard to learn a diffi-cult reward function for a complicated problem, such as a soccer player in the game where the goal of scoring is not directly related to the mission or the role the player is asked to play by the coach. Besides, the tabular method for approximation of returns in RL is more suitable for an environment with less states. In a huge state space, RL methods always face the curse of dimensionality. To alleviate those difficulties, this paper proposed an algorithm â a deep reinforcement learning method regulated by and gating networks. By the merit of deep learning neural networks, even regarding pixels in an image as states, the latent features can be trained and implicitly extracted layer by layer from raw data. In the proposed method, a composed policy can be obtained by a gating network which regulates the outputs from several deep learning modules, each of which is trained for an individual policy. In this thesis, two video games, flappy bird, and ping-pong is adopted as the testbeds to examinate the performance of the proposed method. In the proposed architecture, each policy module of deep learning is trained by a simple reward functions first. By the gating networks, these simple policies can be composed into a more sophisticated one, so as to accommodate with more complicated tasks. This is akin to the divide-and-conquer strategy. The proposed architecture has two kinds of arrangements and structures. One is called the in-parallel gating network, and the other is called the in-serial network. From the outcomes, itâs observed that both can efficiently shorten the training time. Advisors/Committee Members: Ming-Yi Ju (chair), Yu-Jen Chen (chair), Kao-Shing Hwang (committee member), Jin-Ling Lin (chair).

Subjects/Keywords: Reinforcement Learning; Deep Reinforcement Learning; Deep Learning; Gating network; Neural network

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APA (6^th Edition):

Lin, K. (2017). Deep Reinforcement Learning with a Gating Network. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lin, Kun-da. “Deep Reinforcement Learning with a Gating Network.” 2017. Thesis, NSYSU. Accessed February 17, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lin, Kun-da. “Deep Reinforcement Learning with a Gating Network.” 2017. Web. 17 Feb 2020.

Vancouver:

Lin K. Deep Reinforcement Learning with a Gating Network. [Internet] [Thesis]. NSYSU; 2017. [cited 2020 Feb 17]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin K. Deep Reinforcement Learning with a Gating Network. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

15. Tseng, Yi-Chia. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.

Degree: Master, Electrical Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716

► Reinforcement learning (RL) techniques use a reward function to correct a learning agent to solve sequential decision making problems through interactions with a dynamic environment,… (more)

Subjects/Keywords: Apprenticeship Learning; Feature weight; Inverse Reinforcement learning; Reward function; Reinforcement learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tseng, Y. (2015). An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tseng, Yi-Chia. “An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.” 2015. Thesis, NSYSU. Accessed February 17, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tseng, Yi-Chia. “An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.” 2015. Web. 17 Feb 2020.

Vancouver:

Tseng Y. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. [Internet] [Thesis]. NSYSU; 2015. [cited 2020 Feb 17]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tseng Y. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

16. Lin, Hung-shyuan. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.

Degree: Master, Electrical Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021

► Itâs a study on Reinforcement Learning, learning interaction of agents and dynamic environment to get reward function R, and update the policy, converge learning and… (more)

Subjects/Keywords: Inverse reinforcement learning; Reward function; Fuzzy; Reinforcement learning; AdaBoost; Apprenticeship learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lin, H. (2015). Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lin, Hung-shyuan. “Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.” 2015. Thesis, NSYSU. Accessed February 17, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lin, Hung-shyuan. “Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.” 2015. Web. 17 Feb 2020.

Vancouver:

Lin H. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. [Internet] [Thesis]. NSYSU; 2015. [cited 2020 Feb 17]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin H. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

17. Yunduan, Cui. Practical Model-free Reinforcement Learning in Complex Robot Systems with High Dimensional States : 高次元状態を有する複雑なロボットシステムにおける実用的なモデルフリー強化学習; コウジゲンジョウタイオユウスルフクザツナロボットシステムニオケルジツヨウテキナモデルフリーキョウカガクシュウ.

Degree: 博士(工学), 2017, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/12169

Subjects/Keywords: Reinforcement Learning

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yunduan, C. (2017). Practical Model-free Reinforcement Learning in Complex Robot Systems with High Dimensional States : 高次元状態を有する複雑なロボットシステムにおける実用的なモデルフリー強化学習; コウジゲンジョウタイオユウスルフクザツナロボットシステムニオケルジツヨウテキナモデルフリーキョウカガクシュウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/12169

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yunduan, Cui. “Practical Model-free Reinforcement Learning in Complex Robot Systems with High Dimensional States : 高次元状態を有する複雑なロボットシステムにおける実用的なモデルフリー強化学習; コウジゲンジョウタイオユウスルフクザツナロボットシステムニオケルジツヨウテキナモデルフリーキョウカガクシュウ.” 2017. Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/12169.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yunduan, Cui. “Practical Model-free Reinforcement Learning in Complex Robot Systems with High Dimensional States : 高次元状態を有する複雑なロボットシステムにおける実用的なモデルフリー強化学習; コウジゲンジョウタイオユウスルフクザツナロボットシステムニオケルジツヨウテキナモデルフリーキョウカガクシュウ.” 2017. Web. 17 Feb 2020.

Vancouver:

Yunduan C. Practical Model-free Reinforcement Learning in Complex Robot Systems with High Dimensional States : 高次元状態を有する複雑なロボットシステムにおける実用的なモデルフリー強化学習; コウジゲンジョウタイオユウスルフクザツナロボットシステムニオケルジツヨウテキナモデルフリーキョウカガクシュウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; 2017. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/12169.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yunduan C. Practical Model-free Reinforcement Learning in Complex Robot Systems with High Dimensional States : 高次元状態を有する複雑なロボットシステムにおける実用的なモデルフリー強化学習; コウジゲンジョウタイオユウスルフクザツナロボットシステムニオケルジツヨウテキナモデルフリーキョウカガクシュウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; 2017. Available from: http://hdl.handle.net/10061/12169

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Alberta

18. Dick, Travis B. Policy Gradient Reinforcement Learning Without Regret.

Degree: MS, Department of Computing Science, 2015, University of Alberta

URL: https://era.library.ualberta.ca/files/df65vb663

► This thesis consists of two independent projects, each contributing to a central goal of artificial intelligence research: to build computer systems that are capable of… (more)

▼ This thesis consists of two independent projects, each contributing to a central goal of artificial intelligence research: to build computer systems that are capable of performing tasks and solving problems without problem-specific direction from us, their designers. I focus on two formal learning problems that have a strong mathematical grounding. Many real-world learning problems can be cast as instances of one of these two problems. Whenever our translation from the real to the formal accurately captures the character of the problem, then the mathematical arguments we make about algorithms in the formal setting will approximately hold in the real-world as well. The first project focuses on an open question in the theory of policy gradient reinforcement learning methods. These methods learn by trial and error and decide whether a trial was good or bad by comparing its outcome to a given baseline. The baseline has no impact on the formal asymptotic guarantees for policy gradient methods, but it does alter their finite-time behaviour. This immediately raises the question: which baseline should we use? I propose that the baseline should be chosen such that a certain estimate used internally by policy gradient methods has the smallest error. I prove that, under slightly idealistic assumptions, this baseline gives a good upper bound on the regret of policy gradient methods. I derive closed-form expressions for this baseline in terms of properties of the formal learning problem and the computer's behaviour. The quantities appearing in the closed form expressions are often unknown, so I also propose two algorithms for estimating this baseline from only known quantities. Finally, I present an empirical comparison of commonly used baselines that demonstrates improved performance when using my proposed baseline. The second project focuses on a recently proposed class of formal learning problems that is in the intersection of two fields of computing science research: reinforcement learning and online learning. The considered problems are sometimes called online Markov decision processes, or Markov decision processes with changing rewards. The unique property of this class is that it assumes the computer's environment is adversarial, as though it were playing a game against the computer. This is in contrast to the more common assumption that the environment's behaviour is determined entirely by stochastic models. I propose three new algorithms for learning in Markov decision processes with changing rewards under various conditions. I prove theoretical performance guarantees for each algorithm that either complement or improve the best existing results and that often hold even under weaker assumptions. This comes at the cost of increased (but still polynomial) computational complexity. Finally, in the development and analysis of these algorithms, it was necessary to analyze an approximate version of a well-known optimization algorithm called online mirror ascent. To the best of my knowledge, this is the first rigorous analysis…

Subjects/Keywords: Policy Gradient; Baseline; Reinforcement Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dick, T. B. (2015). Policy Gradient Reinforcement Learning Without Regret. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/df65vb663

Chicago Manual of Style (16^th Edition):

Dick, Travis B. “Policy Gradient Reinforcement Learning Without Regret.” 2015. Masters Thesis, University of Alberta. Accessed February 17, 2020. https://era.library.ualberta.ca/files/df65vb663.

MLA Handbook (7^th Edition):

Dick, Travis B. “Policy Gradient Reinforcement Learning Without Regret.” 2015. Web. 17 Feb 2020.

Vancouver:

Dick TB. Policy Gradient Reinforcement Learning Without Regret. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2020 Feb 17]. Available from: https://era.library.ualberta.ca/files/df65vb663.

Council of Science Editors:

Dick TB. Policy Gradient Reinforcement Learning Without Regret. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/df65vb663

University of Alberta

19. White, Adam, M. DEVELOPING A PREDICTIVE APPROACH TO KNOWLEDGE.

Degree: PhD, Department of Computing Science, 2015, University of Alberta

URL: https://era.library.ualberta.ca/files/bg257h75k

► Understanding how an artificial agent may represent, acquire, update, and use large amounts of knowledge has long been an important research challenge in artificial intelligence.… (more)

▼ Understanding how an artificial agent may represent, acquire, update, and use large amounts of knowledge has long been an important research challenge in artificial intelligence. The quantity of knowledge, or knowing a lot, may be nicely thought of as making and updat- ing many predictions about many different courses of action. This predictive approach to knowledge ensures the knowledge is grounded in and learned from low-level data generated by an autonomous agent interacting with the world. Because predictive knowledge can be maintained without human intervention, its acquisition can potentially scale with available data and computing resources. The idea that knowledge might be expressed as prediction has been explored by Cunningham (1972), Becker (1973), Drescher (1990), Sutton and Tanner (2005), Rafols (2006), and Sutton (2009, 2012). Other uses of predictions include representing state with predictions (Littman, Sutton &, Singh 2002; Boots et al. 2010) and modeling partially observable domains (Talvitie & Singh 2011). Unfortunately, technical challenges related to numerical instability, divergence under off-policy sampling, and com- putational complexity have limited the applicability and scalability of predictive knowledge acquisition in practice. This thesis explores a new approach to representing and acquiring predictive knowledge on a robot. The key idea is that value functions, from reinforcement learning, can be used to represent policy-contingent declarative and goal-oriented predictive knowledge. We use recently developed gradient-TD methods that are compatible with off-policy learning and function approximation to explore the practicality of making and updating many predictions in parallel, while the agent interacts with the world from continuous inputs on a robot. The work described here includes both empirical demonstrations of the effectiveness of our new approach and new algorithmic contributions useful for scaling prediction learning. We demonstrate that our value functions are practically learnable and can encode a variety of knowledge with several experiments—including a demonstration of the psychological phenomenon of nexting, learning predictions with refined termination conditions, learn- ing policy-contingent predictions from off-policy samples, and learning procedural goal- directed knowledge—all on two different robot platforms. Our results demonstrate the po- tential scalability of our approach; making and updating thousands of predictions from hun- dreds of thousands of multi-dimensional data samples, in realtime and on a robot—beyond the scalability of related predictive approaches. We also introduce a new online estimate of off-policy learning progress, and demonstrate its usefulness in tracking the performance of thousands of predictions about hundreds of distinct policies. Finally, we conduct a novel empirical investigation of one of our main learning algorithms, GTD(λ), revealing several new insights of particular relevance to predictive knowledge acquisition. All told, the…

Subjects/Keywords: Reinforcement learning; Robotics; Knowledge

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

White, Adam, M. (2015). DEVELOPING A PREDICTIVE APPROACH TO KNOWLEDGE. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/bg257h75k

Chicago Manual of Style (16^th Edition):

White, Adam, M. “DEVELOPING A PREDICTIVE APPROACH TO KNOWLEDGE.” 2015. Doctoral Dissertation, University of Alberta. Accessed February 17, 2020. https://era.library.ualberta.ca/files/bg257h75k.

MLA Handbook (7^th Edition):

White, Adam, M. “DEVELOPING A PREDICTIVE APPROACH TO KNOWLEDGE.” 2015. Web. 17 Feb 2020.

Vancouver:

White, Adam M. DEVELOPING A PREDICTIVE APPROACH TO KNOWLEDGE. [Internet] [Doctoral dissertation]. University of Alberta; 2015. [cited 2020 Feb 17]. Available from: https://era.library.ualberta.ca/files/bg257h75k.

Council of Science Editors:

White, Adam M. DEVELOPING A PREDICTIVE APPROACH TO KNOWLEDGE. [Doctoral Dissertation]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/bg257h75k

University of Louisville

20. Jacobs, Michael. Personalized anticoagulant management using reinforcement learning.

Degree: M. Eng., 2014, University of Louisville

URL: 10.18297/etd/670 ; https://ir.library.louisville.edu/etd/670

► Introduction: There are many problems with current state-of-the-art protocols for maintenance dosing of the oral anticoagulant agent warfarin used in clinical practice. The two key… (more)

▼ Introduction: There are many problems with current state-of-the-art protocols for maintenance dosing of the oral anticoagulant agent warfarin used in clinical practice. The two key challenges include lack of personalized dose adjustment and the high cost of monitoring the efficacy of the therapy in the form of International Normalized Ratio (INR) measurements. A new dosing algorithm based on the principles of Reinforcement Learning (RL), specifically Q-Learning with functional policy approximation, was created to personalize maintenance dosing of warfarin based on observed INR and to optimize the length of time between INR measurements. This new method will help improve patient’s INR time in therapeutic range (TTR) as well as minimize cost associated with monitoring INR when compared to the current standard of care. Procedure: Using the principles of Reinforcement Learning, an algorithm to control warfarin dosing was created. The algorithm uses 9 different controllers which correspond to 9 different levels of warfarin sensitivity. The algorithm switches between controllers until it selects the controller that most closely resembles the individual patient’s response, and thus the optimal dose change (?Dose) and time between INR measurements (?Time) are personalized for each patient, based on INR observed in the patient. Three simulations were performed using data from 100 artificial patients, generated based on data from real patients, each. The first simulation that was performed was an ideal case scenario (clean simulation where the coefficient of variance (CV) of noise added to the model output = 0) using only the warfarin RL algorithm to prove efficacy. The second simulation was performed using the current standard of care and a CV = 25% to simulate intra-patient variability. The third simulation was performed using the warfarin RL algorithm with a CV = 25%. 180 days were simulated for each patient in each simulation and the measurements that were used to benchmark the efficacy of the therapy were INR time in therapeutic range (TTR) and the number of INR measurements that were taken during simulation. Results: The first simulation yielded a mean TTR = 92.1% with a standard deviation of 4.2%, and had a mean number of INR measurements = 7.94 measurements/patient. The second simulation yielded a mean TTR = 45.3% with a standard deviation of 16.4%, and had a mean number of INR measurements = 12.3 measurements/patient. The third simulation yielded a mean TTR = 51.8% with a standard deviation of 10.8%, and had a mean number of INR measurements = 8.05 measurements/patient. A p-value < .001 suggests that there is a statistically significant difference between the 2 algorithms. Conclusion: Results from the simulations indicate that the warfarin RL algorithm performed better than the standard of care at keeping the patient’s INR in therapeutic range and also reduced the number of INR measurements that were necessary. This algorithm could help improve patient safety by increasing the patient’s INR TTR in the presence of… Advisors/Committee Members: Frieboes, Hermann Bueno.

Subjects/Keywords: Reinforcement learning; Warfarin; Drug dosing

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APA (6^th Edition):

Jacobs, M. (2014). Personalized anticoagulant management using reinforcement learning. (Masters Thesis). University of Louisville. Retrieved from 10.18297/etd/670 ; https://ir.library.louisville.edu/etd/670

Chicago Manual of Style (16^th Edition):

Jacobs, Michael. “Personalized anticoagulant management using reinforcement learning.” 2014. Masters Thesis, University of Louisville. Accessed February 17, 2020. 10.18297/etd/670 ; https://ir.library.louisville.edu/etd/670.

MLA Handbook (7^th Edition):

Jacobs, Michael. “Personalized anticoagulant management using reinforcement learning.” 2014. Web. 17 Feb 2020.

Vancouver:

Jacobs M. Personalized anticoagulant management using reinforcement learning. [Internet] [Masters thesis]. University of Louisville; 2014. [cited 2020 Feb 17]. Available from: 10.18297/etd/670 ; https://ir.library.louisville.edu/etd/670.

Council of Science Editors:

Jacobs M. Personalized anticoagulant management using reinforcement learning. [Masters Thesis]. University of Louisville; 2014. Available from: 10.18297/etd/670 ; https://ir.library.louisville.edu/etd/670

Oregon State University

21. Zhang, Wei, 1960-. Reinforcement learning for job-shop scheduling.

Degree: PhD, Computer Science, 1996, Oregon State University

URL: http://hdl.handle.net/1957/11721

Subjects/Keywords: Reinforcement learning

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APA (6^th Edition):

Zhang, Wei, 1. (1996). Reinforcement learning for job-shop scheduling. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/11721

Chicago Manual of Style (16^th Edition):

Zhang, Wei, 1960-. “Reinforcement learning for job-shop scheduling.” 1996. Doctoral Dissertation, Oregon State University. Accessed February 17, 2020. http://hdl.handle.net/1957/11721.

MLA Handbook (7^th Edition):

Zhang, Wei, 1960-. “Reinforcement learning for job-shop scheduling.” 1996. Web. 17 Feb 2020.

Vancouver:

Zhang, Wei 1. Reinforcement learning for job-shop scheduling. [Internet] [Doctoral dissertation]. Oregon State University; 1996. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/1957/11721.

Council of Science Editors:

Zhang, Wei 1. Reinforcement learning for job-shop scheduling. [Doctoral Dissertation]. Oregon State University; 1996. Available from: http://hdl.handle.net/1957/11721

22. Clark, Kendrick Cheng Go. A Reinforcement Learning Model of the Shepherding Task : 羊飼い課題の強化学習モデル; ヒツジカイカダイノキョウカガクシュウモデル.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/10997

Subjects/Keywords: reinforcement learning

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APA (6^th Edition):

Clark, K. C. G. (n.d.). A Reinforcement Learning Model of the Shepherding Task : 羊飼い課題の強化学習モデル; ヒツジカイカダイノキョウカガクシュウモデル. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/10997

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Clark, Kendrick Cheng Go. “A Reinforcement Learning Model of the Shepherding Task : 羊飼い課題の強化学習モデル; ヒツジカイカダイノキョウカガクシュウモデル.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/10997.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Clark, Kendrick Cheng Go. “A Reinforcement Learning Model of the Shepherding Task : 羊飼い課題の強化学習モデル; ヒツジカイカダイノキョウカガクシュウモデル.” Web. 17 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Clark KCG. A Reinforcement Learning Model of the Shepherding Task : 羊飼い課題の強化学習モデル; ヒツジカイカダイノキョウカガクシュウモデル. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/10997.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Clark KCG. A Reinforcement Learning Model of the Shepherding Task : 羊飼い課題の強化学習モデル; ヒツジカイカダイノキョウカガクシュウモデル. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/10997

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

23. Mauricio Alexandre Parente Burdelis. Temporal Difference Approach in Linearly Solvable Markov Decision Processes : 線形可解マルコフ決定過程における受動的ダイナミクスのモデリングと推定; センケイカカイマルコフケッテイカテイニオケルジュドウテキダイナミクスノモデリングトスイテイ.

Degree: 博士(工学), Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/9189

Subjects/Keywords: Reinforcement learning

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APA (6^th Edition):

Burdelis, M. A. P. (n.d.). Temporal Difference Approach in Linearly Solvable Markov Decision Processes : 線形可解マルコフ決定過程における受動的ダイナミクスのモデリングと推定; センケイカカイマルコフケッテイカテイニオケルジュドウテキダイナミクスノモデリングトスイテイ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/9189

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Burdelis, Mauricio Alexandre Parente. “Temporal Difference Approach in Linearly Solvable Markov Decision Processes : 線形可解マルコフ決定過程における受動的ダイナミクスのモデリングと推定; センケイカカイマルコフケッテイカテイニオケルジュドウテキダイナミクスノモデリングトスイテイ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/9189.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Burdelis, Mauricio Alexandre Parente. “Temporal Difference Approach in Linearly Solvable Markov Decision Processes : 線形可解マルコフ決定過程における受動的ダイナミクスのモデリングと推定; センケイカカイマルコフケッテイカテイニオケルジュドウテキダイナミクスノモデリングトスイテイ.” Web. 17 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Burdelis MAP. Temporal Difference Approach in Linearly Solvable Markov Decision Processes : 線形可解マルコフ決定過程における受動的ダイナミクスのモデリングと推定; センケイカカイマルコフケッテイカテイニオケルジュドウテキダイナミクスノモデリングトスイテイ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/9189.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Burdelis MAP. Temporal Difference Approach in Linearly Solvable Markov Decision Processes : 線形可解マルコフ決定過程における受動的ダイナミクスのモデリングと推定; センケイカカイマルコフケッテイカテイニオケルジュドウテキダイナミクスノモデリングトスイテイ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/9189

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

24. 森本, 淳. Hierarchical Decomposition and Min-max Strategy for Fast and Robust Reinforcement Learning in the Real Environment : 階層分割とMin-max戦略による実環境での高速かつロバストな強化学習; カイソウブンカツト Min-max センリャクニヨルジツカンキョウデノコウソクカツロバストナキョウカガクシュウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/2966

Subjects/Keywords: reinforcement learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

森本, �. (n.d.). Hierarchical Decomposition and Min-max Strategy for Fast and Robust Reinforcement Learning in the Real Environment : 階層分割とMin-max戦略による実環境での高速かつロバストな強化学習; カイソウブンカツト Min-max センリャクニヨルジツカンキョウデノコウソクカツロバストナキョウカガクシュウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/2966

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

森本, 淳. “Hierarchical Decomposition and Min-max Strategy for Fast and Robust Reinforcement Learning in the Real Environment : 階層分割とMin-max戦略による実環境での高速かつロバストな強化学習; カイソウブンカツト Min-max センリャクニヨルジツカンキョウデノコウソクカツロバストナキョウカガクシュウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/2966.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

森本, 淳. “Hierarchical Decomposition and Min-max Strategy for Fast and Robust Reinforcement Learning in the Real Environment : 階層分割とMin-max戦略による実環境での高速かつロバストな強化学習; カイソウブンカツト Min-max センリャクニヨルジツカンキョウデノコウソクカツロバストナキョウカガクシュウ.” Web. 17 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

森本 �. Hierarchical Decomposition and Min-max Strategy for Fast and Robust Reinforcement Learning in the Real Environment : 階層分割とMin-max戦略による実環境での高速かつロバストな強化学習; カイソウブンカツト Min-max センリャクニヨルジツカンキョウデノコウソクカツロバストナキョウカガクシュウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/2966.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

森本 �. Hierarchical Decomposition and Min-max Strategy for Fast and Robust Reinforcement Learning in the Real Environment : 階層分割とMin-max戦略による実環境での高速かつロバストな強化学習; カイソウブンカツト Min-max センリャクニヨルジツカンキョウデノコウソクカツロバストナキョウカガクシュウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/2966

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

25. 南條, 信人. 動的な部分空間生成による価値の逐次推定を行う強化学習法 : An effective reinforcement learning with automatic construction of basis functions and sequential approximation; ドウテキナブブンクウカンセイセイニヨルカチノチクジスイテイオオコナウキョウカガクシュウホウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/4584

Subjects/Keywords: Reinforcement learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

南條, �. (n.d.). 動的な部分空間生成による価値の逐次推定を行う強化学習法 : An effective reinforcement learning with automatic construction of basis functions and sequential approximation; ドウテキナブブンクウカンセイセイニヨルカチノチクジスイテイオオコナウキョウカガクシュウホウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/4584

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

南條, 信人. “動的な部分空間生成による価値の逐次推定を行う強化学習法 : An effective reinforcement learning with automatic construction of basis functions and sequential approximation; ドウテキナブブンクウカンセイセイニヨルカチノチクジスイテイオオコナウキョウカガクシュウホウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/4584.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

南條, 信人. “動的な部分空間生成による価値の逐次推定を行う強化学習法 : An effective reinforcement learning with automatic construction of basis functions and sequential approximation; ドウテキナブブンクウカンセイセイニヨルカチノチクジスイテイオオコナウキョウカガクシュウホウ.” Web. 17 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

南條 �. 動的な部分空間生成による価値の逐次推定を行う強化学習法 : An effective reinforcement learning with automatic construction of basis functions and sequential approximation; ドウテキナブブンクウカンセイセイニヨルカチノチクジスイテイオオコナウキョウカガクシュウホウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/4584.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

南條 �. 動的な部分空間生成による価値の逐次推定を行う強化学習法 : An effective reinforcement learning with automatic construction of basis functions and sequential approximation; ドウテキナブブンクウカンセイセイニヨルカチノチクジスイテイオオコナウキョウカガクシュウホウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/4584

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

26. Rodrigues, Alan de Souza. Model-Free and Model-Based Reinforcement Learning Strategies in the Acquisition of Sequential Behaviors : 系列運動の獲得におけるモデルフリーとモデルベース強化学習戦略; ケイレツウンドウノカクトクニオケルモデルフリートモデルベースキョウカガクシュウセンリャク.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/4681

Subjects/Keywords: Reinforcement Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Rodrigues, A. d. S. (n.d.). Model-Free and Model-Based Reinforcement Learning Strategies in the Acquisition of Sequential Behaviors : 系列運動の獲得におけるモデルフリーとモデルベース強化学習戦略; ケイレツウンドウノカクトクニオケルモデルフリートモデルベースキョウカガクシュウセンリャク. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/4681

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Rodrigues, Alan de Souza. “Model-Free and Model-Based Reinforcement Learning Strategies in the Acquisition of Sequential Behaviors : 系列運動の獲得におけるモデルフリーとモデルベース強化学習戦略; ケイレツウンドウノカクトクニオケルモデルフリートモデルベースキョウカガクシュウセンリャク.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/4681.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Rodrigues, Alan de Souza. “Model-Free and Model-Based Reinforcement Learning Strategies in the Acquisition of Sequential Behaviors : 系列運動の獲得におけるモデルフリーとモデルベース強化学習戦略; ケイレツウンドウノカクトクニオケルモデルフリートモデルベースキョウカガクシュウセンリャク.” Web. 17 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Rodrigues AdS. Model-Free and Model-Based Reinforcement Learning Strategies in the Acquisition of Sequential Behaviors : 系列運動の獲得におけるモデルフリーとモデルベース強化学習戦略; ケイレツウンドウノカクトクニオケルモデルフリートモデルベースキョウカガクシュウセンリャク. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/4681.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Rodrigues AdS. Model-Free and Model-Based Reinforcement Learning Strategies in the Acquisition of Sequential Behaviors : 系列運動の獲得におけるモデルフリーとモデルベース強化学習戦略; ケイレツウンドウノカクトクニオケルモデルフリートモデルベースキョウカガクシュウセンリャク. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/4681

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

27. Morimura, Tetsuro. Efficient Task-independent Reinforcement Learning based on Policy Gradient : 方策勾配に基づく効率の良い課題非依存な強化学習法; ホウサクコウバイニモトヅクコウリツノヨイカダイヒイゾンナキョウカガクシュウホウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/4693

Subjects/Keywords: Reinforcement learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Morimura, T. (n.d.). Efficient Task-independent Reinforcement Learning based on Policy Gradient : 方策勾配に基づく効率の良い課題非依存な強化学習法; ホウサクコウバイニモトヅクコウリツノヨイカダイヒイゾンナキョウカガクシュウホウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/4693

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Morimura, Tetsuro. “Efficient Task-independent Reinforcement Learning based on Policy Gradient : 方策勾配に基づく効率の良い課題非依存な強化学習法; ホウサクコウバイニモトヅクコウリツノヨイカダイヒイゾンナキョウカガクシュウホウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/4693.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Morimura, Tetsuro. “Efficient Task-independent Reinforcement Learning based on Policy Gradient : 方策勾配に基づく効率の良い課題非依存な強化学習法; ホウサクコウバイニモトヅクコウリツノヨイカダイヒイゾンナキョウカガクシュウホウ.” Web. 17 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Morimura T. Efficient Task-independent Reinforcement Learning based on Policy Gradient : 方策勾配に基づく効率の良い課題非依存な強化学習法; ホウサクコウバイニモトヅクコウリツノヨイカダイヒイゾンナキョウカガクシュウホウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/4693.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Morimura T. Efficient Task-independent Reinforcement Learning based on Policy Gradient : 方策勾配に基づく効率の良い課題非依存な強化学習法; ホウサクコウバイニモトヅクコウリツノヨイカダイヒイゾンナキョウカガクシュウホウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/4693

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

28. Otsuka, Makoto. Goal-Oriented Representations of the External World : A Free-Energy-Based Approach : 目的指向的な外界の表現に関する研究 : 自由エネルギーからのアプローチ; モクテキシコウテキナガイカイノヒョウゲンニカンスルケンキュウ : ジユウエネルギーカラノアプローチ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/5548

Subjects/Keywords: reinforcement learning

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Otsuka, M. (n.d.). Goal-Oriented Representations of the External World : A Free-Energy-Based Approach : 目的指向的な外界の表現に関する研究 : 自由エネルギーからのアプローチ; モクテキシコウテキナガイカイノヒョウゲンニカンスルケンキュウ : ジユウエネルギーカラノアプローチ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/5548

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Otsuka, Makoto. “Goal-Oriented Representations of the External World : A Free-Energy-Based Approach : 目的指向的な外界の表現に関する研究 : 自由エネルギーからのアプローチ; モクテキシコウテキナガイカイノヒョウゲンニカンスルケンキュウ : ジユウエネルギーカラノアプローチ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/5548.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Otsuka, Makoto. “Goal-Oriented Representations of the External World : A Free-Energy-Based Approach : 目的指向的な外界の表現に関する研究 : 自由エネルギーからのアプローチ; モクテキシコウテキナガイカイノヒョウゲンニカンスルケンキュウ : ジユウエネルギーカラノアプローチ.” Web. 17 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Otsuka M. Goal-Oriented Representations of the External World : A Free-Energy-Based Approach : 目的指向的な外界の表現に関する研究 : 自由エネルギーからのアプローチ; モクテキシコウテキナガイカイノヒョウゲンニカンスルケンキュウ : ジユウエネルギーカラノアプローチ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/5548.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Otsuka M. Goal-Oriented Representations of the External World : A Free-Energy-Based Approach : 目的指向的な外界の表現に関する研究 : 自由エネルギーからのアプローチ; モクテキシコウテキナガイカイノヒョウゲンニカンスルケンキュウ : ジユウエネルギーカラノアプローチ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/5548

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

29. Koyanagi, Izumi. Reinforcement Learning-based Lightpath Establishment in All-Optical WDM Networks : 全光WDM網における強化学習を用いた光パス設定法; ゼンコウ WDM モウニオケルキョウカガクシュウオモチイタヒカリパスセッテイホウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/5630

Subjects/Keywords: Reinforcement learning

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Koyanagi, I. (n.d.). Reinforcement Learning-based Lightpath Establishment in All-Optical WDM Networks : 全光WDM網における強化学習を用いた光パス設定法; ゼンコウ WDM モウニオケルキョウカガクシュウオモチイタヒカリパスセッテイホウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/5630

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Koyanagi, Izumi. “Reinforcement Learning-based Lightpath Establishment in All-Optical WDM Networks : 全光WDM網における強化学習を用いた光パス設定法; ゼンコウ WDM モウニオケルキョウカガクシュウオモチイタヒカリパスセッテイホウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/5630.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Koyanagi, Izumi. “Reinforcement Learning-based Lightpath Establishment in All-Optical WDM Networks : 全光WDM網における強化学習を用いた光パス設定法; ゼンコウ WDM モウニオケルキョウカガクシュウオモチイタヒカリパスセッテイホウ.” Web. 17 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Koyanagi I. Reinforcement Learning-based Lightpath Establishment in All-Optical WDM Networks : 全光WDM網における強化学習を用いた光パス設定法; ゼンコウ WDM モウニオケルキョウカガクシュウオモチイタヒカリパスセッテイホウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/5630.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Koyanagi I. Reinforcement Learning-based Lightpath Establishment in All-Optical WDM Networks : 全光WDM網における強化学習を用いた光パス設定法; ゼンコウ WDM モウニオケルキョウカガクシュウオモチイタヒカリパスセッテイホウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/5630

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

30. Rodrigues, Alan de Souza. Multiple Reinforcement Learning Action Selection Strategies in Prefrontal-Basal Ganglia and Cerebellar Networks : 強化学習理論に基づく意思決定戦略における前頭前野－大脳基底核－小脳系神経回路の計算論的機能に関する研究; キョウカガクシュウリロンニモトズクイシケッテイセンリャクニオケルゼントウゼンヤダイノウキテイカクショウノウケイシンケイカイロノケイサンロンテキキノウニカンスルケンキュウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/6637

Subjects/Keywords: reinforcement learning

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Rodrigues, A. d. S. (n.d.). Multiple Reinforcement Learning Action Selection Strategies in Prefrontal-Basal Ganglia and Cerebellar Networks : 強化学習理論に基づく意思決定戦略における前頭前野－大脳基底核－小脳系神経回路の計算論的機能に関する研究; キョウカガクシュウリロンニモトズクイシケッテイセンリャクニオケルゼントウゼンヤダイノウキテイカクショウノウケイシンケイカイロノケイサンロンテキキノウニカンスルケンキュウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/6637

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Rodrigues, Alan de Souza. “Multiple Reinforcement Learning Action Selection Strategies in Prefrontal-Basal Ganglia and Cerebellar Networks : 強化学習理論に基づく意思決定戦略における前頭前野－大脳基底核－小脳系神経回路の計算論的機能に関する研究; キョウカガクシュウリロンニモトズクイシケッテイセンリャクニオケルゼントウゼンヤダイノウキテイカクショウノウケイシンケイカイロノケイサンロンテキキノウニカンスルケンキュウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 17, 2020. http://hdl.handle.net/10061/6637.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Rodrigues, Alan de Souza. “Multiple Reinforcement Learning Action Selection Strategies in Prefrontal-Basal Ganglia and Cerebellar Networks : 強化学習理論に基づく意思決定戦略における前頭前野－大脳基底核－小脳系神経回路の計算論的機能に関する研究; キョウカガクシュウリロンニモトズクイシケッテイセンリャクニオケルゼントウゼンヤダイノウキテイカクショウノウケイシンケイカイロノケイサンロンテキキノウニカンスルケンキュウ.” Web. 17 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Rodrigues AdS. Multiple Reinforcement Learning Action Selection Strategies in Prefrontal-Basal Ganglia and Cerebellar Networks : 強化学習理論に基づく意思決定戦略における前頭前野－大脳基底核－小脳系神経回路の計算論的機能に関する研究; キョウカガクシュウリロンニモトズクイシケッテイセンリャクニオケルゼントウゼンヤダイノウキテイカクショウノウケイシンケイカイロノケイサンロンテキキノウニカンスルケンキュウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10061/6637.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Rodrigues AdS. Multiple Reinforcement Learning Action Selection Strategies in Prefrontal-Basal Ganglia and Cerebellar Networks : 強化学習理論に基づく意思決定戦略における前頭前野－大脳基底核－小脳系神経回路の計算論的機能に関する研究; キョウカガクシュウリロンニモトズクイシケッテイセンリャクニオケルゼントウゼンヤダイノウキテイカクショウノウケイシンケイカイロノケイサンロンテキキノウニカンスルケンキュウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/6637

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

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Open Access Theses and Dissertations