Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model

TL;DR

This paper demonstrates that model-based reinforcement learning algorithms can achieve minimax-optimal sample complexity with significantly fewer samples than previously thought, breaking the longstanding sample size barrier in MDPs.

Contribution

It establishes the first minimax-optimal sample complexity guarantees for model-based RL algorithms that work across all feasible sample sizes, including smaller ones.

Findings

Overcomes the sample size barrier for infinite-horizon MDPs.

Certifies minimax optimality of two model-based algorithms.

Extends results to finite-horizon MDPs with plain planning.

Abstract

This paper is concerned with the sample efficiency of reinforcement learning, assuming access to a generative model (or simulator). We first consider $\gamma$-discounted infinite-horizon Markov decision processes (MDPs) with state space $\mathcal{S}$ and action space $\mathcal{A}$. Despite a number of prior works tackling this problem, a complete picture of the trade-offs between sample complexity and statistical accuracy is yet to be determined. In particular, all prior results suffer from a severe sample size barrier, in the sense that their claimed statistical guarantees hold only when the sample size exceeds at least $\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)^2}$. The current paper overcomes this barrier by certifying the minimax optimality of two algorithms -- a perturbed model-based algorithm and a conservative model-based algorithm -- as soon as the sample size exceeds the order of $\frac{|\mathcal{S}||\mathcal{A}|}{1-\gamma}$ (modulo some log factor). Moving beyond infinite-horizon MDPs, we further study time-inhomogeneous finite-horizon MDPs, and prove that a plain model-based planning algorithm suffices to achieve minimax-optimal sample complexity given any target accuracy level. To the best of our knowledge, this work delivers the first minimax-optimal guarantees that accommodate the entire range of sample sizes (beyond which finding a meaningful policy is information theoretically infeasible).