A Family of Robust Stochastic Operators for Reinforcement Learning

NeurIPS 2019 Yingdong LuMark SquillanteChai Wah Wu

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... (read more)

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