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2022 AAAI AAAI 2022

A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes

Abstract

Abstract This paper presents a model-free reinforcement learning (RL) algorithm for infinite-horizon average-reward Constrained Markov Decision Processes (CMDPs). Considering a learning horizon K, which is sufficiently large, the proposed algorithm achieves sublinear regret and zero constraint violation. The bounds depend on the number of states S, the number of actions A, and two constants which are independent of the learning horizon K.

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization and Reinforcement Learning
🐝 Cross-Pollinator — Artificial Intelligence, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy

Authors

Honghao Wei , Xin Liu , Lei Ying

Topics

Machine Learning > Optimization & Theory > Learning Theory Reinforcement Learning > Methods > Policy Learning Machine Learning > Learning Types > Reinforcement Learning Mathematics & Optimization > Optimization > Optimization

Keywords

constrained markov decision process sublinear regret average reward model-free reinforcement learning constraint violation constrained mdp model-free algorithm infinite horizon
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