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Logarithmic regret bounds for continuous-time average-reward Markov decision processes. (arXiv:2205.11168v2 [cs.LG] UPDATED)
May 25, 2022, 1:11 a.m. | Xuefeng Gao, Xun Yu Zhou
stat.ML updates on arXiv.org arxiv.org
We consider reinforcement learning for continuous-time Markov decision
processes (MDPs) in the infinite-horizon, average-reward setting. In contrast
to discrete-time MDPs, a continuous-time process moves to a state and stays
there for a random holding time after an action is taken. With unknown
transition probabilities and rates of exponential holding times, we derive
instance-dependent regret lower bounds that are logarithmic in the time
horizon. Moreover, we design a learning algorithm and establish a finite-time
regret bound that achieves the logarithmic growth …
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