Learning Near Optimal Policies with Low Inherent Bellman Error

Jul 12, 2020

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We study the exploration problem with approximate linear action-value functions in episodic reinforcement learning under the notion of low inherent Bellman error, a condition normally employed to show convergence of approximate value iteration. We relate this condition to other common frameworks and show that it is strictly more general than the low rank (or linear) MDP assumption of prior work. We provide an algorithm with a rate optimal regret bound for this setting. While computational tractability questions remain open, this enriches the class of MDPs with a linear representation for the action-value function where statistically efficient reinforcement learning is possible.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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