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Title Inverse reinforcement learning of risk-sensitive utility
Publication Date
Date Available
Date Accessioned
Degree MS
Discipline/Department Computer Science
Degree Level masters
University/Publisher University of Georgia
Abstract The uncertain and stochastic nature of the real world poses a challenge for autonomous cars in making decisions to ensure appropriate motion, considering the safety of the passengers and other cars that may or may not be autonomous. It is crucial for these systems to learn driving patterns of other vehicles from their environment in order to predict their movement for a better decision making. In this research, we focus on solving the highway merging problem, where an autonomous vehicle tries to merge onto a highway by using Inverse Reinforcement Learning. Human behavior is complex, and both linear and exponential utility functions fail to capture the non-linearity associated with such decision making. To resolve this issue, we model such behavior with a One-Switch utility function. We present an Inverse Reinforcement Learning technique that allows an autonomous vehicle to predict human driving patterns to efficiently merge onto a highway by modeling risk with a one- switch utility function.
Subjects/Keywords Inverse Reinforcement Learning
Contributors Prashant Doshi
Language en
Rights public
Country of Publication us
Record ID oai:ugakr.libs.uga.edu:10724/36698
Repository uga
Date Retrieved
Date Indexed 2017-03-31
Note [degree] MS; [department] Computer Science; [major] Computer Science; [advisor] Prashant Doshi; [committee] Prashant Doshi;

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…4 2 Background 5 2.1 Utility Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Markov Decision Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Inverse Reinforcement Learning

…function is a class of utility functions that can model such decision makers using cumulative rewards. We apply Inverse Reinforcement Learning (IRL) on Markov Decision Process (MDP) framework in learning the reward functions that…

…We have developed an Inverse Reinforcement Learning (IRL) technique for learning a non-decomposable one-switch (1s) utility function. 3 We are validating the Inverse Reinforcement Learning algorithm with the help of a demo problem…

…document has been structured into nine chapters. Chapter 2 gives a brief review of Markov Decision Processes, Utility Theory and Inverse Reinforcement Learning, which are the building blocks of both the problem and the solution domain. Chapter 3 covers the…

…utility theory and one switch utility functions followed by Markov Decision Process (MDP), which is a framework for sequential decision making under uncertain environments. Finally, we introduce Inverse Reinforcement Learning (IRL) and…

…s, a, s0 ))] (2.5) s0 ∈S 2.3 Inverse Reinforcement Learning In Inverse Reinforcement Learning (IRL), we primarily deal with two kinds of agents, namely the expert and the learner. The expert is assumed to be…

…generally as possible”. The resultant distribution matches the given constraints, but is otherwise completely unbiased. In case of Inverse Reinforcement Learning, we formulate this problem as finding the distribution over policies such that the feature…

…making is sub-rational. In the current thesis, we direct our attention towards learning a risk prone behavior of the human drivers. To address this issue, we designed a car system, referred as the ABC car system. Figure 1.1 is the diagrammatic…