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

No. 4: AAAI-22 Technical Tracks 4

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 36

AAAI Technical Track on Constraint Satisfaction and Optimization

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.

10.1609/aaai.v36i4.20302