A Family of Robust Stochastic Operators for Reinforcement Learning

Yingdong Lu; Mark Squillante; Chai Wah Wu

2019 NIPS NeurIPS 2019

A Family of Robust Stochastic Operators for Reinforcement Learning

Abstract

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.

🌉 Interdisciplinary Bridge — Machine Learning and Reinforcement Learning

🧭 Keyword Pioneer — stochastic operator

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Yingdong Lu , Mark Squillante , Chai Wah Wu

Topics

Machine Learning > Optimization & Theory > Stochastic Processes Reinforcement Learning > Methods > Deep RL Reinforcement Learning > Methods > Policy Learning Machine Learning > Learning Types > Reinforcement Learning Machine Learning > Learning Types > Robustness

Keywords

reinforcement learning robust reinforcement learning value function approximation bellman operator stochastic operator action gap

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