rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions

NeurIPS 2018 Mathieu FehrOlivier BuffetVincent ThomasJilles Dibangoye

Many state-of-the-art algorithms for solving Partially Observable Markov Decision Processes (POMDPs) rely on turning the problem into a “fully observable” problem—a belief MDP—and exploiting the piece-wise linearity and convexity (PWLC) of the optimal value function in this new state space (the belief simplex ∆). This approach has been extended to solving ρ-POMDPs—i.e., for information-oriented criteria—when the reward ρ is convex in ∆... (read more)

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