Retrieval-Augmented Decision Transformer: External Memory for In-context RL

TL;DR

The paper introduces RA-DT, a retrieval-augmented decision transformer that uses external memory to improve in-context reinforcement learning, especially in complex environments with long episodes and sparse rewards.

Contribution

RA-DT employs a domain-agnostic external memory mechanism for retrieving relevant experiences, enabling effective in-context RL in complex environments with long episodes.

Findings

RA-DT outperforms baselines on grid-world environments.

RA-DT uses only a fraction of the context length compared to existing methods.

The retrieval component does not require training and is domain-agnostic.

Abstract

In-context learning (ICL) is the ability of a model to learn a new task by observing a few exemplars in its context. While prevalent in NLP, this capability has recently also been observed in Reinforcement Learning (RL) settings. Prior in-context RL methods, however, require entire episodes in the agent's context. Given that complex environments typically lead to long episodes with sparse rewards, these methods are constrained to simple environments with short episodes. To address these challenges, we introduce Retrieval-Augmented Decision Transformer (RA-DT). RA-DT employs an external memory mechanism to store past experiences from which it retrieves only sub-trajectories relevant for the current situation. The retrieval component in RA-DT does not require training and can be entirely domain-agnostic. We evaluate the capabilities of RA-DT on grid-world environments, robotics simulations, and procedurally-generated video games. On grid-worlds, RA-DT outperforms baselines, while using only a fraction of their context length. Furthermore, we illuminate the limitations of current in-context RL methods on complex environments and discuss future directions. To facilitate future research, we release datasets for four of the considered environments.