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Safe Reinforcement Learning in Constrained Markov Decision Processes
[article]
2020
arXiv
pre-print
Safe reinforcement learning has been a promising approach for optimizing the policy of an agent that operates in safety-critical applications. In this paper, we propose an algorithm, SNO-MDP, that explores and optimizes Markov decision processes under unknown safety constraints. Specifically, we take a stepwise approach for optimizing safety and cumulative reward. In our method, the agent first learns safety constraints by expanding the safe region, and then optimizes the cumulative reward in
arXiv:2008.06626v1
fatcat:fpblmiihknckfn6fghde23mc2m