Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization
This work addresses efficient exploration and generalization in reinforcement learning for deterministic systems, offering theoretical guarantees but is incremental in nature.
The paper tackles reinforcement learning in deterministic systems by proposing the optimistic constraint propagation (OCP) algorithm, which achieves optimal actions in all but at most K episodes when the true value function is in the hypothesis class, with K defined by the eluder dimension.
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 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 class, for the special case where the hypothesis class is the span of pre-specified indicator functions over disjoint sets. We also discuss the computational complexity of OCP and present computational results involving two illustrative examples.