How the level sampling process impacts zero-shot generalisation in deep reinforcement learning

TL;DR

This paper explores how different level sampling strategies in deep reinforcement learning influence zero-shot generalisation, revealing that adaptive and self-supervised methods can improve generalisation by controlling overfitting and over-generalisation.

Contribution

It introduces SSED, a self-supervised environment design approach that reduces mutual information and improves zero-shot generalisation in RL agents.

Findings

Adaptive sampling based on value loss reduces overfitting.

UED methods can cause over-generalisation and degrade ZSG.

SSED improves ZSG performance significantly.

Abstract

A key limitation preventing the wider adoption of autonomous agents trained via deep reinforcement learning (RL) is their limited ability to generalise to new environments, even when these share similar characteristics with environments encountered during training. In this work, we investigate how a non-uniform sampling strategy of individual environment instances, or levels, affects the zero-shot generalisation (ZSG) ability of RL agents, considering two failure modes: overfitting and over-generalisation. As a first step, we measure the mutual information (MI) between the agent's internal representation and the set of training levels, which we find to be well-correlated to instance overfitting. In contrast to uniform sampling, adaptive sampling strategies prioritising levels based on their value loss are more effective at maintaining lower MI, which provides a novel theoretical justification for this class of techniques. We then turn our attention to unsupervised environment design (UED) methods, which adaptively generate new training levels and minimise MI more effectively than methods sampling from a fixed set. However, we find UED methods significantly shift the training distribution, resulting in over-generalisation and worse ZSG performance over the distribution of interest. To prevent both instance overfitting and over-generalisation, we introduce self-supervised environment design (SSED). SSED generates levels using a variational autoencoder, effectively reducing MI while minimising the shift with the distribution of interest, and leads to statistically significant improvements in ZSG over fixed-set level sampling strategies and UED methods.