ICED: Zero-Shot Transfer in Reinforcement Learning via In-Context Environment Design

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 …

agents autonomous autonomous agents context cs.ai cs.lg design environment environments instances reinforcement reinforcement learning sampling training transfer via work zero-shot