Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization

• 成果类型: Article
• 署名作者: Wen, Zheng; Van Roy, Benjamin
• 署名单位: Adobe Systems Inc.; Stanford University
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.2016.0826
• 发表日期: 2017
• 页码: 762-782
• 关键词: Bounds

摘要

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 a given hypothesis class, OCP selects optimal actions over all but at most D episodes, where D 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 class, for the special case where the hypothesis class is the span of prespecified indicator functions over disjoint sets. We also discuss the computational complexity of OCP and present computational results involving two illustrative examples.