Improved Corruption Robust Algorithms for Episodic Reinforcement Learning

ICML, pp. 1561-1570, 2021.

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Abstract:

We study episodic reinforcement learning under unknown adversarial corruptions in both the rewards and the transition probabilities of the underlying system. We propose new algorithms which, compared to the existing results in (Lykouris et al., 2020), achieve strictly better regret bounds in terms of total corruptions for the tabular se...More

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