  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.
