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Title: A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes
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.  more » « less
Award ID(s):
2001687 2002608
NSF-PAR ID:
10342263
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
36
Issue:
4
ISSN:
2159-5399
Page Range / eLocation ID:
3868 to 3876
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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