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Bellman Gradient Iteration for Inverse Reinforcement Learning

Li, Kun and Sui, Yanan and Burdick, Joel W. (2017) Bellman Gradient Iteration for Inverse Reinforcement Learning. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20190410-120640737

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Abstract

This paper develops an inverse reinforcement learning algorithm aimed at recovering a reward function from the observed actions of an agent. We introduce a strategy to flexibly handle different types of actions with two approximations of the Bellman Optimality Equation, and a Bellman Gradient Iteration method to compute the gradient of the Q-value with respect to the reward function. These methods allow us to build a differentiable relation between the Q-value and the reward function and learn an approximately optimal reward function with gradient methods. We test the proposed method in two simulated environments by evaluating the accuracy of different approximations and comparing the proposed method with existing solutions. The results show that even with a linear reward function, the proposed method has a comparable accuracy with the state-of-the-art method adopting a non-linear reward function, and the proposed method is more flexible because it is defined on observed actions instead of trajectories.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1707.07767arXivDiscussion Paper
Additional Information:This work was supported by the National Institutes of Health, NIBIB.
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Record Number:CaltechAUTHORS:20190410-120640737
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190410-120640737
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94635
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:10 Apr 2019 19:52
Last Modified:03 Oct 2019 21:05

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