Poster
A Family of Robust Stochastic Operators for Reinforcement Learning
Yingdong Lu · Mark Squillante · Chai Wah Wu
East Exhibition Hall B + C #199
Keywords: [ Stochastic Methods ] [ Algorithms ] [ Reinforcement Learning and Planning ] [ Reinforcement Learning ]
We consider a new family of stochastic operators for reinforcement learning with the goal of alleviating negative effects and becoming more robust to approximation or estimation errors. Various theoretical results are established, which include showing that our family of operators preserve optimality and increase the action gap in a stochastic sense. Our empirical results illustrate the strong benefits of our robust stochastic operators, significantly outperforming the classical Bellman operator and recently proposed operators.
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