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Efficient Planning in Large MDPs with Weak Linear Function Approximation
Roshan Shariff · Csaba Szepesvari

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #542

Large-scale Markov decision processes (MDPs) require planning algorithms with runtime independent of the number of states of the MDP. We consider the planning problem in MDPs using linear value function approximation with only weak requirements: low approximation error for the optimal value function, and a small set of “core” states whose features span those of other states. In particular, we make no assumptions about the representability of policies or value functions of non-optimal policies. Our algorithm produces almost-optimal actions for any state using a generative oracle (simulator) for the MDP, while its computation time scales polynomially with the number of features, core states, and actions and the effective horizon.

Author Information

Roshan Shariff (University of Alberta)
Csaba Szepesvari (DeepMind / University of Alberta)

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