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Efficient Exploration and Value Function Generalization in Deterministic Systems

2020-01-16 | |  75 |   57 |   0

Abstract

We consider the problem of reinforcement learning over episodes of a finitehorizon 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 Q? lies within the hypothesis class Q, OCP selects optimal actions over all but at most dimE [Q] episodes, where dimE denotes the eluder dimension. We establish further efficiency and asymptotic performance guarantees that apply even if Q? does not lie in Q, for the special case where Q is the span of pre-specified indicator functions over disjoint sets.

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