A General Family of Robust Stochastic Operators for Reinforcement Learning

By Yingdong Lu, Mark S. Squillante, Chai Wah Wu. 2018

3 citations recorded by the source corpus.

We consider a new family of operators for reinforcement learning with the goal of alleviating the negative effects and becoming more robust to approximation or estimation errors. Various theoretical results are established, which include showing on a sample path basis that our family of operators preserve optimality and increase the action gap. Our empirical results illustrate the strong benefits of our family of operators, significantly outperforming the classical Bellman operator and recently proposed operators.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Reinforcement Learning

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In Artificial Intelligence

In Computational Theory and Mathematics

In Reinforcement Learning

Citations in the corpus

Conservative Q-Learning for Offline Reinforcement Learning (2020, background): “4, is related to how gap-increasing Bellman backup operators [6, 37] are more robust to estimation error in online RL.”