Instabilities of Offline RL with Pre-Trained Neural Representation

TL;DR

This paper empirically investigates the stability of offline reinforcement learning using pre-trained neural representations, revealing that significant error amplification occurs unless the data distribution closely matches the target policy, highlighting the need for stronger conditions.

Contribution

It provides an empirical analysis of offline RL stability with pre-trained features, demonstrating limitations and the necessity for mild distribution shifts for reliable performance.

Findings

Offline RL exhibits substantial error amplification with pre-trained representations.

Stability in offline RL is only achieved under extremely mild distribution shifts.

Strong representational conditions are required for successful offline RL beyond supervised learning conditions.

Abstract

In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substantially differs from that of the target policy to be evaluated. Recent theoretical advances have shown that such sample-efficient offline RL is indeed possible provided certain strong representational conditions hold, else there are lower bounds exhibiting exponential error amplification (in the problem horizon) unless the data collection distribution has only a mild distribution shift relative to the target policy. This work studies these issues from an empirical perspective to gauge how stable offline RL methods are. In particular, our methodology explores these ideas when using features from pre-trained neural networks, in the hope that these representations are powerful enough to permit sample efficient offline RL. Through extensive experiments on a range of tasks, we see that substantial error amplification does occur even when using such pre-trained representations (trained on the same task itself); we find offline RL is stable only under extremely mild distribution shift. The implications of these results, both from a theoretical and an empirical perspective, are that successful offline RL (where we seek to go beyond the low distribution shift regime) requires substantially stronger conditions beyond those which suffice for successful supervised learning.