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Shangmin-GuoICML
2024
Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas, Stefano V. Albrecht
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design
International Conference on Machine Learning, 2024
Abstract | BibTex | arXiv
ICMLdeep-rl
Abstract:
Autonomous agents trained using deep reinforcement learning (RL) often lack the ability to successfully generalise to new environments, even when they share characteristics with the environments they have encountered during training. In this work, we investigate how the sampling of individual environment instances, or levels, affects the zero-shot generalisation (ZSG) ability of RL agents. We discover that, for deep actor-critic architectures sharing their base layers, prioritising levels according to their value loss minimises the mutual information between the agent's internal representation and the set of training levels in the generated training data. This provides a novel theoretical justification for the implicit regularisation achieved by certain adaptive sampling strategies. We then turn our attention to unsupervised environment design (UED) methods, which have more control over the data generation mechanism. We find that existing UED methods can significantly shift the training distribution, which translates to low ZSG performance. To prevent both overfitting and distributional shift, we introduce data-regularised environment design (DRED). DRED generates levels using a generative model trained over an initial set of level parameters, reducing distributional shift, and achieves significant improvements in ZSG over adaptive level sampling strategies and UED methods.
@inproceedings{garcin2024dred,
title={{DRED}: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design},
author={Samuel Garcin and James Doran and Shangmin Guo and Christopher G. Lucas and Stefano V. Albrecht},
year={2024},
booktitle={International Conference on Machine Learning (ICML)}
}