Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation

TL;DR

This paper introduces LoRa-PI, a model-free reinforcement learning algorithm that efficiently learns near-optimal policies in low-rank structured systems using a novel leverage score-based matrix estimation method.

Contribution

The paper presents a new low-rank matrix estimation technique with entry-wise guarantees that do not depend on matrix coherence, enabling order-optimal sample complexity in reinforcement learning.

Findings

Achieves order-optimal sample complexity in learning policies.

Introduces leverage score-based sampling for matrix estimation.

Provides guarantees independent of matrix coherence.

Abstract

We consider the problem of learning an $\varepsilon$-optimal policy in controlled dynamical systems with low-rank latent structure. For this problem, we present LoRa-PI (Low-Rank Policy Iteration), a model-free learning algorithm alternating between policy improvement and policy evaluation steps. In the latter, the algorithm estimates the low-rank matrix corresponding to the (state, action) value function of the current policy using the following two-phase procedure. The entries of the matrix are first sampled uniformly at random to estimate, via a spectral method, the leverage scores of its rows and columns. These scores are then used to extract a few important rows and columns whose entries are further sampled. The algorithm exploits these new samples to complete the matrix estimation using a CUR-like method. For this leveraged matrix estimation procedure, we establish entry-wise guarantees that remarkably, do not depend on the coherence of the matrix but only on its spikiness. These guarantees imply that LoRa-PI learns an $\varepsilon$-optimal policy using $\widetilde{O}({S+A\over \mathrm{poly}(1-\gamma)\varepsilon^2})$ samples where $S$ (resp. $A$) denotes the number of states (resp. actions) and $\gamma$ the discount factor. Our algorithm achieves this order-optimal (in $S$, $A$ and $\varepsilon$) sample complexity under milder conditions than those assumed in previously proposed approaches.