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Efficient Exploration and Value Function Generalization in Deterministic Systems
Zheng Wen · Benjamin Van Roy

Thu Dec 05 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

We consider the problem of reinforcement learning over episodes of a finite-horizon deterministic system and as a solution propose optimistic constraint propagation (OCP), an algorithm designed to synthesize efficient exploration and value function generalization. We establish that when the true value function lies within the given hypothesis class, OCP selects optimal actions over all but at most K episodes, where K is the eluder dimension of the given hypothesis class. We establish further efficiency and asymptotic performance guarantees that apply even if the true value function does not lie in the given hypothesis space, for the special case where the hypothesis space is the span of pre-specified indicator functions over disjoint sets.

Author Information

Zheng Wen (Stanford University)
Benjamin Van Roy (Stanford University)

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