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Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning. (arXiv:2201.07958v1 [cs.LG])
Jan. 21, 2022, 2:10 a.m. | Jaeyoung Lee, Sean Sedwards, Krzysztof Czarnecki
cs.LG updates on arXiv.org arxiv.org
We consider the challenge of finding a deterministic policy for a Markov
decision process that uniformly (in all states) maximizes one reward subject to
a probabilistic constraint over a different reward. Existing solutions do not
fully address our precise problem definition, which nevertheless arises
naturally in the context of safety-critical robotic systems. This class of
problem is known to be hard, but the combined requirements of determinism and
uniform optimality can create learning instability. In this work, after
describing and …
More from arxiv.org / cs.LG updates on arXiv.org
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