Track 1: Robust Offline Learning via Adversarial World Models

Online reinforcement learning (RL), a setting in which an agent learns directly from interactions with the environment, has shown remarkable improvements with the advent of tools enabling faster and more efficient parallel training. However, learning a policy in an offline setting, where the agent is trained from trajectories collected earlier on an environment, has yet to make similar strides. World modelling, where a representation of the underlying environment is trained from offline data, enables an agent to sample trajectories from a learned model without ever interacting with the true environment. Policies learned in world models are often brittle as agents frequently learn to exploit inaccuracies in the world model rather than to behave according to the true underlying dynamics. Methods under the umbrella of Unsupervised Environment Design (UED) address robustness by designing a ‘difficult but solvable’ autocurriculum for the agent. Unfortunately, UED has been confined to simple environments definable by a set of selected parameters. This paper presents a novel approach that integrates the robustness of UED with the descriptive power of world models, achieving strong test-time performance in a range of environments while training using only offline data. Our approach provides benefits in both sparse and rich data regimes. As such, it offers huge potential for decision making in real-world settings without the need for any expensive real-world sampling.