Online Markov Decision Processes with Non-oblivious Strategic Adversary
Le Cong Dinh,
David Henry Mguni,
Long Tran-Thanh
et al.
Abstract:We study a novel setting in Online Markov Decision Processes (OMDPs) where the loss function is chosen by a nonoblivious strategic adversary who follows a no-external regret algorithm. In this setting, we first demonstrate that MDP-Expert, an existing algorithm that works well with oblivious adversaries can still apply and achieve a policy regret bound of O ( log(where is the size of adversary's pure strategy set and | | denotes the size of agent's action space. Considering real-world games where the support s… Show more
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