Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
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We consider the problem of learning in episodic finite-horizon Markov decision processes with unknown transition function, bandit feedback, and adversarial losses. We propose an efficient algorithm that achieves Õ(L|X|^2√(|A|T)) regret with high probability, where L is the horizon, |X| is the number of states, |A| is the number of actions, and T is the number of episodes. To the best of our knowledge, our algorithm is the first one to ensure sub-linear regret in this challenging setting. Our key technical contribution is to introduce an optimistic loss estimator that is inversely weighted by an upper occupancy bound.
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