subject:(reinforcement)

Oregon State University

1. 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 27, 2021. http://hdl.handle.net/1957/13662.

MLA Handbook (7^th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Web. 27 Feb 2021.

Vancouver:

Proper S. Scaling multiagent reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2021 Feb 27]. 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

Oregon State University

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

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 27, 2021. http://hdl.handle.net/1957/25199.

MLA Handbook (7^th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Web. 27 Feb 2021.

Vancouver:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2021 Feb 27]. 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

University of Waikato

3. Lord, Sarah. Dog Behaviour - Effect of Delay To Reinforcement .

Degree: 2012, University of Waikato

URL: http://hdl.handle.net/10289/7039

► Dogs were fed dry food as reinforcement and were required to touch a wand with their nose to get that reinforcement. In the first study,… (more)

Subjects/Keywords: Reinforcement; Delay

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APA (6^th Edition):

Lord, S. (2012). Dog Behaviour - Effect of Delay To Reinforcement . (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/7039

Chicago Manual of Style (16^th Edition):

Lord, Sarah. “Dog Behaviour - Effect of Delay To Reinforcement .” 2012. Masters Thesis, University of Waikato. Accessed February 27, 2021. http://hdl.handle.net/10289/7039.

MLA Handbook (7^th Edition):

Lord, Sarah. “Dog Behaviour - Effect of Delay To Reinforcement .” 2012. Web. 27 Feb 2021.

Vancouver:

Lord S. Dog Behaviour - Effect of Delay To Reinforcement . [Internet] [Masters thesis]. University of Waikato; 2012. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10289/7039.

Council of Science Editors:

Lord S. Dog Behaviour - Effect of Delay To Reinforcement . [Masters Thesis]. University of Waikato; 2012. Available from: http://hdl.handle.net/10289/7039

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 27, 2021. 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. 27 Feb 2021.

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 2021 Feb 27]. 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

5. 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 27, 2021. 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. 27 Feb 2021.

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 2021 Feb 27]. 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

Delft University of Technology

6. Tian, Yuan (author). Model Free Reinforcement Learning with Stability Guarantee.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c

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Model-free reinforcement learning has proved to be successful in many tasks such as robotic manipulator, video games, and even stock trading. However, as the dynamics… (more)

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Model-free reinforcement learning has proved to be successful in many tasks such as robotic manipulator, video games, and even stock trading. However, as the dynamics of the environment is unmodelled, it is fundamentally difficult to ensure the learned policy to be absolutely reliable and its performance is guaranteed. In this thesis, we borrow the concept of stability and Lyapunov analysis in control theory to design a policy with stability guarantee and assure the guaranteed behaviors of the agent. A novel sample-based approach is proposed for analyzing the stability of a learning control system, and on the basis of the theoretical result, we establish a practical model-free learning framework with provable stability, safety and performance guarantees. % Specifically, a novel locally constrained method is proposed to solve the safety constrained problems with lower conservatism. In our solution, a Lyapunov function is searched automatically to guarantee the closed-loop system stability, which also guides the simultaneous learning (covering both the policy and value-based learning methods). Our approach is evaluated on a series of discrete and continuous control benchmarks and largely outperforms the state-of-the-art results concerning unconstrained and constrained problems. It is also shown that the algorithm has the ability of recovery to equilibrium under perturbation using the policy with stability guarantee. (Anonymous code is available to reproduce the experimental esults\footnote{\url{https://github.com/RLControlTheoreticGuarantee/Guarantee_Learning_Control}}.) Since sometimes the constraint is hard to define, we introduce a novel method to learn a constraint by representing the bad cases or situations as a distribution, and the constraint is the Wasserstein distance between the distribution.

Mechanical Engineering | Vehicle Engineering

Advisors/Committee Members: Pan, Wei (mentor), Zhou, Hongpeng (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Reinforcement Learning

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APA (6^th Edition):

Tian, Y. (. (2019). Model Free Reinforcement Learning with Stability Guarantee. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c

Chicago Manual of Style (16^th Edition):

Tian, Yuan (author). “Model Free Reinforcement Learning with Stability Guarantee.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c.

MLA Handbook (7^th Edition):

Tian, Yuan (author). “Model Free Reinforcement Learning with Stability Guarantee.” 2019. Web. 27 Feb 2021.

Vancouver:

Tian Y(. Model Free Reinforcement Learning with Stability Guarantee. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c.

Council of Science Editors:

Tian Y(. Model Free Reinforcement Learning with Stability Guarantee. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c

Delft University of Technology

7. Van Vliet, B. (author). Accelerating flat reinforcement learning on a robot by using subgoals in a hierarchical framework.

Degree: 2010, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:8dccd17c-4483-4abc-9d9b-540c801186ce

►

Learning a motor skill task with Reinforcement Learning still takes a long time. A way to speed up the learning process with- out using much… (more)

Subjects/Keywords: Reinforcement

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

APA (6^th Edition):

Van Vliet, B. (. (2010). Accelerating flat reinforcement learning on a robot by using subgoals in a hierarchical framework. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:8dccd17c-4483-4abc-9d9b-540c801186ce

Chicago Manual of Style (16^th Edition):

Van Vliet, B (author). “Accelerating flat reinforcement learning on a robot by using subgoals in a hierarchical framework.” 2010. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:8dccd17c-4483-4abc-9d9b-540c801186ce.

MLA Handbook (7^th Edition):

Van Vliet, B (author). “Accelerating flat reinforcement learning on a robot by using subgoals in a hierarchical framework.” 2010. Web. 27 Feb 2021.

Vancouver:

Van Vliet B(. Accelerating flat reinforcement learning on a robot by using subgoals in a hierarchical framework. [Internet] [Masters thesis]. Delft University of Technology; 2010. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:8dccd17c-4483-4abc-9d9b-540c801186ce.

Council of Science Editors:

Van Vliet B(. Accelerating flat reinforcement learning on a robot by using subgoals in a hierarchical framework. [Masters Thesis]. Delft University of Technology; 2010. Available from: http://resolver.tudelft.nl/uuid:8dccd17c-4483-4abc-9d9b-540c801186ce

Delft University of Technology

8. Van Rooijen, J.C. (author). 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

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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)

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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.

Systems and Control

Delft Center for Systems and Control

Mechanical, Maritime and Materials Engineering

Advisors/Committee Members: Grondman, I. (mentor), Babuska, R. (mentor).

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 (author). “Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords.” 2012. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38.

MLA Handbook (7^th Edition):

Van Rooijen, J C (author). “Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords.” 2012. Web. 27 Feb 2021.

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 2021 Feb 27]. 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

Delft University of Technology

9. Van Diepen, M.D.M. (author). Avoiding failure states during reinforcement learning.

Degree: 2011, Delft University of Technology

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

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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)

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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.

Biorobotics

BioMechanical Engineering

Mechanical, Maritime and Materials Engineering

Advisors/Committee Members: Jonker, P.P. (mentor), Schuitema, E. (mentor).

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 (author). “Avoiding failure states during reinforcement learning.” 2011. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6.

MLA Handbook (7^th Edition):

Van Diepen, M D M (author). “Avoiding failure states during reinforcement learning.” 2011. Web. 27 Feb 2021.

Vancouver:

Van Diepen MDM(. Avoiding failure states during reinforcement learning. [Internet] [Masters thesis]. Delft University of Technology; 2011. [cited 2021 Feb 27]. 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

University of Illinois – Urbana-Champaign

10. 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 27, 2021. 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. 27 Feb 2021.

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 2021 Feb 27]. 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

11. Bodin, Vanessa G. A Framework for Analyzing Cultural Considerations in Working with Young Clients Diagnosed with Autism Spectrum Disorder.

Degree: Applied Behavior Analysis: M.S., Community Psychology, Counseling and Family Therapy, 2019, St. Cloud State University

URL: https://repository.stcloudstate.edu/cpcf_etds/68

► Culture consists of beliefs, laws, customs, and manners that society deems to be socially appropriate. As culture influences how the individual behaves within society,… (more)

Subjects/Keywords: culture; autism; edible reinforcement; praise; positive reinforcement

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APA (6^th Edition):

Bodin, V. G. (2019). A Framework for Analyzing Cultural Considerations in Working with Young Clients Diagnosed with Autism Spectrum Disorder. (Masters Thesis). St. Cloud State University. Retrieved from https://repository.stcloudstate.edu/cpcf_etds/68

Chicago Manual of Style (16^th Edition):

Bodin, Vanessa G. “A Framework for Analyzing Cultural Considerations in Working with Young Clients Diagnosed with Autism Spectrum Disorder.” 2019. Masters Thesis, St. Cloud State University. Accessed February 27, 2021. https://repository.stcloudstate.edu/cpcf_etds/68.

MLA Handbook (7^th Edition):

Bodin, Vanessa G. “A Framework for Analyzing Cultural Considerations in Working with Young Clients Diagnosed with Autism Spectrum Disorder.” 2019. Web. 27 Feb 2021.

Vancouver:

Bodin VG. A Framework for Analyzing Cultural Considerations in Working with Young Clients Diagnosed with Autism Spectrum Disorder. [Internet] [Masters thesis]. St. Cloud State University; 2019. [cited 2021 Feb 27]. Available from: https://repository.stcloudstate.edu/cpcf_etds/68.

Council of Science Editors:

Bodin VG. A Framework for Analyzing Cultural Considerations in Working with Young Clients Diagnosed with Autism Spectrum Disorder. [Masters Thesis]. St. Cloud State University; 2019. Available from: https://repository.stcloudstate.edu/cpcf_etds/68

SUNY College at Brockport

12. Rumfola, Lindsey. Positive Reinforcement Positively Helps Students in the Classroom.

Degree: MSEd, Education and Human Development, 2017, SUNY College at Brockport

URL: https://digitalcommons.brockport.edu/ehd_theses/786

► Current teachers are struggling between choosing mature teaching strategies and choosing more contemporary strategies to utilize in their classrooms. Although the choice to the… (more)

Subjects/Keywords: positive; reinforcement; positive reinforcement; behavior; students; Education

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APA (6^th Edition):

Rumfola, L. (2017). Positive Reinforcement Positively Helps Students in the Classroom. (Thesis). SUNY College at Brockport. Retrieved from https://digitalcommons.brockport.edu/ehd_theses/786

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):

Rumfola, Lindsey. “Positive Reinforcement Positively Helps Students in the Classroom.” 2017. Thesis, SUNY College at Brockport. Accessed February 27, 2021. https://digitalcommons.brockport.edu/ehd_theses/786.

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

MLA Handbook (7^th Edition):

Rumfola, Lindsey. “Positive Reinforcement Positively Helps Students in the Classroom.” 2017. Web. 27 Feb 2021.

Vancouver:

Rumfola L. Positive Reinforcement Positively Helps Students in the Classroom. [Internet] [Thesis]. SUNY College at Brockport; 2017. [cited 2021 Feb 27]. Available from: https://digitalcommons.brockport.edu/ehd_theses/786.

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

Council of Science Editors:

Rumfola L. Positive Reinforcement Positively Helps Students in the Classroom. [Thesis]. SUNY College at Brockport; 2017. Available from: https://digitalcommons.brockport.edu/ehd_theses/786

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

University of North Texas

13. Reetz, Stephany Kristina. The Use of Conjugate Reinforcement in Autism Treatment Programs: a Demonstration and Discussion.

Degree: 2012, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc177243/

► The effect of a reinforcer on behavior is largely determined by the schedule in which it is implemented. One type of reinforcement schedule that has… (more)

Subjects/Keywords: Conjugate reinforcement; reinforcement schedule; autism treatment

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APA (6^th Edition):

Reetz, S. K. (2012). The Use of Conjugate Reinforcement in Autism Treatment Programs: a Demonstration and Discussion. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc177243/

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):

Reetz, Stephany Kristina. “The Use of Conjugate Reinforcement in Autism Treatment Programs: a Demonstration and Discussion.” 2012. Thesis, University of North Texas. Accessed February 27, 2021. https://digital.library.unt.edu/ark:/67531/metadc177243/.

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

MLA Handbook (7^th Edition):

Reetz, Stephany Kristina. “The Use of Conjugate Reinforcement in Autism Treatment Programs: a Demonstration and Discussion.” 2012. Web. 27 Feb 2021.

Vancouver:

Reetz SK. The Use of Conjugate Reinforcement in Autism Treatment Programs: a Demonstration and Discussion. [Internet] [Thesis]. University of North Texas; 2012. [cited 2021 Feb 27]. Available from: https://digital.library.unt.edu/ark:/67531/metadc177243/.

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

Council of Science Editors:

Reetz SK. The Use of Conjugate Reinforcement in Autism Treatment Programs: a Demonstration and Discussion. [Thesis]. University of North Texas; 2012. Available from: https://digital.library.unt.edu/ark:/67531/metadc177243/

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

Louisiana State University

14. Alvarez, Jessica Pearl. Fixed-Time Schedule Effects on Participant Responding: An Evaluation Of Similar vs. Dissimilar Schedule Programs Using a Group Design Approach.

Degree: PhD, Psychology, 2014, Louisiana State University

URL: etd-06302014-190504 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3565

► We evaluated the effectiveness of fixed-time (FT) schedules of reinforcement at eliminating participant’s responding using a between-subjects group design. Participants experienced one of three conditions;… (more)

Subjects/Keywords: fixed-time schedules; reinforcement; noncontingent reinforcement

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APA (6^th Edition):

Alvarez, J. P. (2014). Fixed-Time Schedule Effects on Participant Responding: An Evaluation Of Similar vs. Dissimilar Schedule Programs Using a Group Design Approach. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-06302014-190504 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3565

Chicago Manual of Style (16^th Edition):

Alvarez, Jessica Pearl. “Fixed-Time Schedule Effects on Participant Responding: An Evaluation Of Similar vs. Dissimilar Schedule Programs Using a Group Design Approach.” 2014. Doctoral Dissertation, Louisiana State University. Accessed February 27, 2021. etd-06302014-190504 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3565.

MLA Handbook (7^th Edition):

Alvarez, Jessica Pearl. “Fixed-Time Schedule Effects on Participant Responding: An Evaluation Of Similar vs. Dissimilar Schedule Programs Using a Group Design Approach.” 2014. Web. 27 Feb 2021.

Vancouver:

Alvarez JP. Fixed-Time Schedule Effects on Participant Responding: An Evaluation Of Similar vs. Dissimilar Schedule Programs Using a Group Design Approach. [Internet] [Doctoral dissertation]. Louisiana State University; 2014. [cited 2021 Feb 27]. Available from: etd-06302014-190504 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3565.

Council of Science Editors:

Alvarez JP. Fixed-Time Schedule Effects on Participant Responding: An Evaluation Of Similar vs. Dissimilar Schedule Programs Using a Group Design Approach. [Doctoral Dissertation]. Louisiana State University; 2014. Available from: etd-06302014-190504 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3565

15. Sundvall, Jukka. Flexibility of reinforcement biases and reaction times in competitive zero-sum games.

Degree: PhD, 2020, University of Sussex

URL: http://sro.sussex.ac.uk/id/eprint/91253/ ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.804990

► In competitive zero-sum games with mixed equilibria, two rational players should make each of their game choices randomly, with no contingencies between their choices. However,… (more)

▼ In competitive zero-sum games with mixed equilibria, two rational players should make each of their game choices randomly, with no contingencies between their choices. However, people often deviate from this equilibrium by following a reinforcement heuristic of repeating moves that won on the previous round (win-stay) and avoiding the repetition of moves that did not win (lose-shift). In this thesis, I examine the flexibility of these reinforcement biases, and the speed of decision-making: under what circumstances do people make biased choices, and under what circumstances do people choose quickly or stop to deliberate? In Chapter 1, I review the current state of knowledge on how well people can produce or detect randomness, how reinforcement biases influence decision-making, and how processing speeds might differ between different game situations. In Chapters 2 and 3 I present four experiments where I examined performance in the games Rock, Paper & Scissors (RPS; Chapter 2, Experiments 1 and 2) and Matching Pennies (MP; Chapter 3, Experiments 3 and 4). Surprisingly, I found no reinforcement biases in RPS, but consistent reinforcement biases in MP. Additionally, participants made slower decisions after losses when they succeeded in the game due to finding an appropriate strategy to exploit an opponent's pattern (Chapter 2), but not when they succeeded no matter what they did (Chapter 3). In Chapter 4, I present two experiments (Experiments 5 and 6) directly comparing performance in RPS and MP, designed to replicate the findings from Chapters 2 and 3, and examine why the previous studies only found reinforcement biases in MP. The results of these two last experiments suggest that reinforcement biases differ between RPS and MP due to different cognitive demands, and that there is considerable variability in reinforcement biases both between individuals and between the two types of bias. In Chapter 5, I discuss the contributions of the findings on the wider literature on bias and randomness detection, the generality of the reinforcement biases, and present some suggestions for future studies.

Subjects/Keywords: BF0319.5.R4 Reinforcement

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APA (6^th Edition):

Sundvall, J. (2020). Flexibility of reinforcement biases and reaction times in competitive zero-sum games. (Doctoral Dissertation). University of Sussex. Retrieved from http://sro.sussex.ac.uk/id/eprint/91253/ ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.804990

Chicago Manual of Style (16^th Edition):

Sundvall, Jukka. “Flexibility of reinforcement biases and reaction times in competitive zero-sum games.” 2020. Doctoral Dissertation, University of Sussex. Accessed February 27, 2021. http://sro.sussex.ac.uk/id/eprint/91253/ ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.804990.

MLA Handbook (7^th Edition):

Sundvall, Jukka. “Flexibility of reinforcement biases and reaction times in competitive zero-sum games.” 2020. Web. 27 Feb 2021.

Vancouver:

Sundvall J. Flexibility of reinforcement biases and reaction times in competitive zero-sum games. [Internet] [Doctoral dissertation]. University of Sussex; 2020. [cited 2021 Feb 27]. Available from: http://sro.sussex.ac.uk/id/eprint/91253/ ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.804990.

Council of Science Editors:

Sundvall J. Flexibility of reinforcement biases and reaction times in competitive zero-sum games. [Doctoral Dissertation]. University of Sussex; 2020. Available from: http://sro.sussex.ac.uk/id/eprint/91253/ ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.804990

Anna University

16. Rajagopal S. Investigations on the effect of Reinforcement in exterior beamcolumn Joints under reversal loading;.

Degree: Investigations on the effect of Reinforcement in exterior beamcolumn Joints under reversal loading, 2015, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/49573

►

Beam column joints are recognized as the most critical and newlinevulnerable zone of reinforced concrete RC moment resisting structure newlinesubject to seismic loads During an… (more)

Subjects/Keywords: Reinforcement in exterior

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APA (6^th Edition):

S, R. (2015). Investigations on the effect of Reinforcement in exterior beamcolumn Joints under reversal loading;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/49573

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):

S, Rajagopal. “Investigations on the effect of Reinforcement in exterior beamcolumn Joints under reversal loading;.” 2015. Thesis, Anna University. Accessed February 27, 2021. http://shodhganga.inflibnet.ac.in/handle/10603/49573.

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

MLA Handbook (7^th Edition):

S, Rajagopal. “Investigations on the effect of Reinforcement in exterior beamcolumn Joints under reversal loading;.” 2015. Web. 27 Feb 2021.

Vancouver:

S R. Investigations on the effect of Reinforcement in exterior beamcolumn Joints under reversal loading;. [Internet] [Thesis]. Anna University; 2015. [cited 2021 Feb 27]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/49573.

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

Council of Science Editors:

S R. Investigations on the effect of Reinforcement in exterior beamcolumn Joints under reversal loading;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/49573

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

Delft University of Technology

17. Keulen, Bart (author). Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422

► An important problem in reinforcement learning is the exploration-exploitation dilemma. Especially for environments with sparse or misleading rewards it has proven difficult to construct a… (more)

▼ An important problem in reinforcement learning is the exploration-exploitation dilemma. Especially for environments with sparse or misleading rewards it has proven difficult to construct a good exploration strategy. For discrete domains good exploration strategies have been devised, but are often nontrivial to implement on more complex domains with continuous states and/or actions. In this work, a novel persistent and directed exploration framework is developed, called Smart Start. Usually, a reinforcement learning agent executes its learned policy with some exploration strategy from the start until the end of an episode, which we call ``normal'' learning. The idea of Smart Start is to split a reinforcement learning episode in two parts, the Smart Start phase and the ``normal'' learning phase. The initial Smart Start phase guides the agent to a region in which the agent expects to learn the most. The region is constructed using previous experiences and the guiding is done using a model-based planning or trajectory optimization method. When the agent arrives at the region, it continues its ``normal'' reinforcement learning. This approach leaves the performance of the used reinforcement learning algorithm unchanged, but augments it with persistent and directed exploration. The Smart Start framework was evaluated using three reinforcement learning algorithms, a simple model-based reinforcement learning algorithm (MBRL), R-max and Q-Learning with epsilon-greedy, Boltzmann and UCB1 exploration. The evaluation was done on four discrete gridworld environments. Three environments with sparse rewards and one with misleading rewards. We showed that the performance of Q-Learning with Smart Start is comparable to R-max, which performs near optimal in the used scenarios. The MBRL algorithm with Smart Start is even able to outperform R-max in some of the problems. We show that Smart Start is a good framework for exploration that can be incorporated with any reinforcement learning algorithm. Advisors/Committee Members: Kober, Jens (mentor), Pratt, Jerry (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Reinforcement Learning; Exploration

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

APA (6^th Edition):

Keulen, B. (. (2018). Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422

Chicago Manual of Style (16^th Edition):

Keulen, Bart (author). “Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422.

MLA Handbook (7^th Edition):

Keulen, Bart (author). “Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning.” 2018. Web. 27 Feb 2021.

Vancouver:

Keulen B(. Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422.

Council of Science Editors:

Keulen B(. Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422

University of Manitoba

18. Schnerch, Gabriel. Establishing conditioned reinforcers in individuals with developmental disabilities: comparison of the stimulus-stimulus pairing procedure and the discriminative control procedure.

Degree: Psychology, 2014, University of Manitoba

URL: http://hdl.handle.net/1993/30814

► Individuals with developmental disabilities may be sensitive to a limited number of conditioned reinforcers. Therefore, the development of effective procedures to establish conditioned reinforcers in… (more)

Subjects/Keywords: Reinforcement; Pairing; Conditioning

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APA (6^th Edition):

Schnerch, G. (2014). Establishing conditioned reinforcers in individuals with developmental disabilities: comparison of the stimulus-stimulus pairing procedure and the discriminative control procedure. (Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/30814

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):

Schnerch, Gabriel. “Establishing conditioned reinforcers in individuals with developmental disabilities: comparison of the stimulus-stimulus pairing procedure and the discriminative control procedure.” 2014. Thesis, University of Manitoba. Accessed February 27, 2021. http://hdl.handle.net/1993/30814.

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

MLA Handbook (7^th Edition):

Schnerch, Gabriel. “Establishing conditioned reinforcers in individuals with developmental disabilities: comparison of the stimulus-stimulus pairing procedure and the discriminative control procedure.” 2014. Web. 27 Feb 2021.

Vancouver:

Schnerch G. Establishing conditioned reinforcers in individuals with developmental disabilities: comparison of the stimulus-stimulus pairing procedure and the discriminative control procedure. [Internet] [Thesis]. University of Manitoba; 2014. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1993/30814.

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

Council of Science Editors:

Schnerch G. Establishing conditioned reinforcers in individuals with developmental disabilities: comparison of the stimulus-stimulus pairing procedure and the discriminative control procedure. [Thesis]. University of Manitoba; 2014. Available from: http://hdl.handle.net/1993/30814

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

University of Illinois – Urbana-Champaign

19. Van Stralen, Neale A. Hierarchical reinforcement learning for adaptive and autonomous decision-making in robotics.

Degree: MS, Aerospace Engineering, 2020, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/108536

► In recent years, Reinforcement Learning has been able to solve extremely complex games in simulation, but with limited success in deployment to real-world scenarios. The… (more)

Subjects/Keywords: Reinforcement Learning; Adaptation

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APA (6^th Edition):

Van Stralen, N. A. (2020). Hierarchical reinforcement learning for adaptive and autonomous decision-making in robotics. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/108536

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):

Van Stralen, Neale A. “Hierarchical reinforcement learning for adaptive and autonomous decision-making in robotics.” 2020. Thesis, University of Illinois – Urbana-Champaign. Accessed February 27, 2021. http://hdl.handle.net/2142/108536.

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

MLA Handbook (7^th Edition):

Van Stralen, Neale A. “Hierarchical reinforcement learning for adaptive and autonomous decision-making in robotics.” 2020. Web. 27 Feb 2021.

Vancouver:

Van Stralen NA. Hierarchical reinforcement learning for adaptive and autonomous decision-making in robotics. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2020. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2142/108536.

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

Council of Science Editors:

Van Stralen NA. Hierarchical reinforcement learning for adaptive and autonomous decision-making in robotics. [Thesis]. University of Illinois – Urbana-Champaign; 2020. Available from: http://hdl.handle.net/2142/108536

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

University of North Texas

20. Coulter, Laura E. The Effects of Increasing Rates of Reinforcement Through an Alternative Fluent Behavior on the Acquisition and Extinction of Behavior in Dogs.

Degree: 2012, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc149576/

► The purpose of the present study was to experimentally investigate the effects of interspersing the opportunity to perform a fluent behavior during the acquisition of… (more)

Subjects/Keywords: Interspersed; reinforcement; extinction

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APA (6^th Edition):

Coulter, L. E. (2012). The Effects of Increasing Rates of Reinforcement Through an Alternative Fluent Behavior on the Acquisition and Extinction of Behavior in Dogs. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc149576/

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):

Coulter, Laura E. “The Effects of Increasing Rates of Reinforcement Through an Alternative Fluent Behavior on the Acquisition and Extinction of Behavior in Dogs.” 2012. Thesis, University of North Texas. Accessed February 27, 2021. https://digital.library.unt.edu/ark:/67531/metadc149576/.

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

MLA Handbook (7^th Edition):

Coulter, Laura E. “The Effects of Increasing Rates of Reinforcement Through an Alternative Fluent Behavior on the Acquisition and Extinction of Behavior in Dogs.” 2012. Web. 27 Feb 2021.

Vancouver:

Coulter LE. The Effects of Increasing Rates of Reinforcement Through an Alternative Fluent Behavior on the Acquisition and Extinction of Behavior in Dogs. [Internet] [Thesis]. University of North Texas; 2012. [cited 2021 Feb 27]. Available from: https://digital.library.unt.edu/ark:/67531/metadc149576/.

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

Council of Science Editors:

Coulter LE. The Effects of Increasing Rates of Reinforcement Through an Alternative Fluent Behavior on the Acquisition and Extinction of Behavior in Dogs. [Thesis]. University of North Texas; 2012. Available from: https://digital.library.unt.edu/ark:/67531/metadc149576/

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

Louisiana State University

21. Lomas Mevers, Joanna Elizabeth. Treatment integrity failures matched to behavioral function.

Degree: PhD, Psychology, 2013, Louisiana State University

URL: etd-06192013-135121 ; https://digitalcommons.lsu.edu/gradschool_dissertations/357

► Prior research on treatment integrity has focused on either the lack of measurement of the independent variable (Peterson, Homer & Wonderlich, 1982; Gresham, Gansle &… (more)

Subjects/Keywords: compliance; prompting; reinforcement

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APA (6^th Edition):

Lomas Mevers, J. E. (2013). Treatment integrity failures matched to behavioral function. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-06192013-135121 ; https://digitalcommons.lsu.edu/gradschool_dissertations/357

Chicago Manual of Style (16^th Edition):

Lomas Mevers, Joanna Elizabeth. “Treatment integrity failures matched to behavioral function.” 2013. Doctoral Dissertation, Louisiana State University. Accessed February 27, 2021. etd-06192013-135121 ; https://digitalcommons.lsu.edu/gradschool_dissertations/357.

MLA Handbook (7^th Edition):

Lomas Mevers, Joanna Elizabeth. “Treatment integrity failures matched to behavioral function.” 2013. Web. 27 Feb 2021.

Vancouver:

Lomas Mevers JE. Treatment integrity failures matched to behavioral function. [Internet] [Doctoral dissertation]. Louisiana State University; 2013. [cited 2021 Feb 27]. Available from: etd-06192013-135121 ; https://digitalcommons.lsu.edu/gradschool_dissertations/357.

Council of Science Editors:

Lomas Mevers JE. Treatment integrity failures matched to behavioral function. [Doctoral Dissertation]. Louisiana State University; 2013. Available from: etd-06192013-135121 ; https://digitalcommons.lsu.edu/gradschool_dissertations/357

Louisiana State University

22. Alvarez, Jessica Pearl. Comparing extinction and noncontingent reinforcement under challenging conditions: the discriminative effects of reinforcement delivery.

Degree: MA, Psychology, 2011, Louisiana State University

URL: etd-03202012-125334 ; https://digitalcommons.lsu.edu/gradschool_theses/4178

► Extinction (EXT) and noncontingent reinforcement (NCR) procedures are commonly used to reduce problem behavior maintained by operant reinforcement. Both procedures reduce behavior by eliminating the… (more)

Subjects/Keywords: noncontingent reinforcement; extinction

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APA (6^th Edition):

Alvarez, J. P. (2011). Comparing extinction and noncontingent reinforcement under challenging conditions: the discriminative effects of reinforcement delivery. (Masters Thesis). Louisiana State University. Retrieved from etd-03202012-125334 ; https://digitalcommons.lsu.edu/gradschool_theses/4178

Chicago Manual of Style (16^th Edition):

Alvarez, Jessica Pearl. “Comparing extinction and noncontingent reinforcement under challenging conditions: the discriminative effects of reinforcement delivery.” 2011. Masters Thesis, Louisiana State University. Accessed February 27, 2021. etd-03202012-125334 ; https://digitalcommons.lsu.edu/gradschool_theses/4178.

MLA Handbook (7^th Edition):

Alvarez, Jessica Pearl. “Comparing extinction and noncontingent reinforcement under challenging conditions: the discriminative effects of reinforcement delivery.” 2011. Web. 27 Feb 2021.

Vancouver:

Alvarez JP. Comparing extinction and noncontingent reinforcement under challenging conditions: the discriminative effects of reinforcement delivery. [Internet] [Masters thesis]. Louisiana State University; 2011. [cited 2021 Feb 27]. Available from: etd-03202012-125334 ; https://digitalcommons.lsu.edu/gradschool_theses/4178.

Council of Science Editors:

Alvarez JP. Comparing extinction and noncontingent reinforcement under challenging conditions: the discriminative effects of reinforcement delivery. [Masters Thesis]. Louisiana State University; 2011. Available from: etd-03202012-125334 ; https://digitalcommons.lsu.edu/gradschool_theses/4178

University of New South Wales

23. 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 (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 27, 2021. 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. 27 Feb 2021.

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 2021 Feb 27]. 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

Virginia Tech

24. Isernia, Lindsay Taylor. Impact of Training Method on Behavioral, Physiological, and Relationship Measures in Horses.

Degree: MS, Animal and Poultry Sciences, 2021, Virginia Tech

URL: http://hdl.handle.net/10919/101781

► The equine world has started using positive reinforcement (R+), such as providing treats. Often horses experience a combination of negative reinforcement (R-) and R+, such… (more)

Subjects/Keywords: Riding horses; rehomed Thoroughbreds; positive reinforcement; negative reinforcement; combination reinforcement

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APA (6^th Edition):

Isernia, L. T. (2021). Impact of Training Method on Behavioral, Physiological, and Relationship Measures in Horses. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/101781

Chicago Manual of Style (16^th Edition):

Isernia, Lindsay Taylor. “Impact of Training Method on Behavioral, Physiological, and Relationship Measures in Horses.” 2021. Masters Thesis, Virginia Tech. Accessed February 27, 2021. http://hdl.handle.net/10919/101781.

MLA Handbook (7^th Edition):

Isernia, Lindsay Taylor. “Impact of Training Method on Behavioral, Physiological, and Relationship Measures in Horses.” 2021. Web. 27 Feb 2021.

Vancouver:

Isernia LT. Impact of Training Method on Behavioral, Physiological, and Relationship Measures in Horses. [Internet] [Masters thesis]. Virginia Tech; 2021. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10919/101781.

Council of Science Editors:

Isernia LT. Impact of Training Method on Behavioral, Physiological, and Relationship Measures in Horses. [Masters Thesis]. Virginia Tech; 2021. Available from: http://hdl.handle.net/10919/101781

Northeastern University

25. Flint, Kimberly. A comparison of edible, social, and no contrived reinforcement on the acquisition of a behavior chain.

Degree: MS, Department of Counseling and Applied Educational Psychology, 2011, Northeastern University

URL: http://hdl.handle.net/2047/d20001146

► Reinforcement is used by applied behavior analysts to increase the future frequency of responding. Finding effective reinforcers is important to increase the frequency of behavior.… (more)

Subjects/Keywords: behavioral sciences; comparing reinforcers; contrived reinforcement; reinforcement; Reinforcement (Psychology); Educational psychology; Child Psychology

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APA (6^th Edition):

Flint, K. (2011). A comparison of edible, social, and no contrived reinforcement on the acquisition of a behavior chain. (Masters Thesis). Northeastern University. Retrieved from http://hdl.handle.net/2047/d20001146

Chicago Manual of Style (16^th Edition):

Flint, Kimberly. “A comparison of edible, social, and no contrived reinforcement on the acquisition of a behavior chain.” 2011. Masters Thesis, Northeastern University. Accessed February 27, 2021. http://hdl.handle.net/2047/d20001146.

MLA Handbook (7^th Edition):

Flint, Kimberly. “A comparison of edible, social, and no contrived reinforcement on the acquisition of a behavior chain.” 2011. Web. 27 Feb 2021.

Vancouver:

Flint K. A comparison of edible, social, and no contrived reinforcement on the acquisition of a behavior chain. [Internet] [Masters thesis]. Northeastern University; 2011. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2047/d20001146.

Council of Science Editors:

Flint K. A comparison of edible, social, and no contrived reinforcement on the acquisition of a behavior chain. [Masters Thesis]. Northeastern University; 2011. Available from: http://hdl.handle.net/2047/d20001146

Delft University of Technology

26. Kovács, B. (author). Safe reinforcement learning in long-horizon partially observable environments.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:83556b37-1484-41f4-b168-3bd316def4a0

► Deep reinforcement learning went through an unprecedented development in the last decade, resulting in agents defeating world champion human players in complex board games like… (more)

▼ Deep reinforcement learning went through an unprecedented development in the last decade, resulting in agents defeating world champion human players in complex board games like go and chess. With few exceptions, deep reinforcement learning research focuses on fully observable environments, while there is slightly less research in the direction of partially observable environments. Though, they are relevant in real-world applications where the physical systems' sensors can read only a limited subset of features required for decision making. In many problems, long-term memory is required for the agent to make optimal decisions, possibly through tens or hundreds of timesteps. In supervised deep learning, in the NLP domain, sequential input processing is done with specialized network architectures like variants of RNNs and attention-based networks. In this work, we investigate the potential of these advanced sequence-processing architectures in the context of deep reinforcement learning for partially observable environments. Additionally, since partial observability widely appears in the physical world, we take a safe approach by trying to limit high exploration costs and damage to the agent and its environment. First, we augment the soft actor-critic method with constraints on the episodic cost, resulting in an objective function with two Lagrangian multiplier: an entropy temperature and a safety temperature. Then, to support long-horizon, partially observable environments, we use gated recurrent (LSTM, GRU) and self-attention based neural networks for the policy and the estimation of Q-functions. We also study how the design choices and hyperparameters of the self-attention based method affect the performance. To evaluate the problem in safety-constrained environments with long-term temporal dependencies, we develop a new set of benchmarks with four parameterizable, partially observable simulations. The environments are also parameterizable with the length of the history containing relevant features. Hence, we can observe how different network architectures handle the same problems with varying time horizons. Additionally, we introduce a practical framework for the reproducible evaluation of the methods. We conclude that both the recurrent and the self-attention based architectures have high application potential in the introduced domains. We confirm that the feedforward network based baseline agent, shows high performance on problems where only a few, or tens of timesteps have to be processed sequentially. The recurrent and self-attention based architectures show their advantage in environments with longer horizons, where the sequence of events play an important role and looking back to a fixed position is not sufficient. Maintaining safety proves to be problematic when the reward and cost functions show correlation. Additionally, strict cost limits usually lead to a poor policy and no exploration, contributing to higher costs on the long term, for some environments. We… Advisors/Committee Members: Spaan, M.T.J. (mentor), Yang, Q. (graduation committee), van Gemert, J.C. (graduation committee), Kober, J. (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: reinforcement learning; self-attention; partially observable; deep reinforcement learning; safe reinforcement learning

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APA (6^th Edition):

Kovács, B. (. (2020). Safe reinforcement learning in long-horizon partially observable environments. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:83556b37-1484-41f4-b168-3bd316def4a0

Chicago Manual of Style (16^th Edition):

Kovács, B (author). “Safe reinforcement learning in long-horizon partially observable environments.” 2020. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:83556b37-1484-41f4-b168-3bd316def4a0.

MLA Handbook (7^th Edition):

Kovács, B (author). “Safe reinforcement learning in long-horizon partially observable environments.” 2020. Web. 27 Feb 2021.

Vancouver:

Kovács B(. Safe reinforcement learning in long-horizon partially observable environments. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:83556b37-1484-41f4-b168-3bd316def4a0.

Council of Science Editors:

Kovács B(. Safe reinforcement learning in long-horizon partially observable environments. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:83556b37-1484-41f4-b168-3bd316def4a0

Oregon State University

27. 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 27, 2021. http://hdl.handle.net/1957/11721.

MLA Handbook (7^th Edition):

Zhang, Wei, 1960-. “Reinforcement learning for job-shop scheduling.” 1996. Web. 27 Feb 2021.

Vancouver:

Zhang, Wei 1. Reinforcement learning for job-shop scheduling. [Internet] [Doctoral dissertation]. Oregon State University; 1996. [cited 2021 Feb 27]. 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

28. 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 27, 2021. 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. 27 Feb 2021.

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 2021 Feb 27]. 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.

29. 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 27, 2021. 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. 27 Feb 2021.

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 2021 Feb 27]. 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.

30. 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

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):

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 27, 2021. 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. 27 Feb 2021.

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 2021 Feb 27]. 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.

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Open Access Theses and Dissertations