Delayed Reinforcement Learning by Imitation

TL;DR

This paper introduces DIDA, a novel imitation learning algorithm designed to address delays in reinforcement learning environments, demonstrating high efficiency and strong performance across various tasks.

Contribution

The paper proposes DIDA, a new algorithm that effectively learns in delayed environments by leveraging undelayed demonstrations, with theoretical bounds and empirical validation.

Findings

DIDA achieves high performance in delayed tasks.

DIDA demonstrates remarkable sample efficiency.

Theoretical bounds relate delayed and undelayed task performance.

Abstract

When the agent's observations or interactions are delayed, classic reinforcement learning tools usually fail. In this paper, we propose a simple yet new and efficient solution to this problem. We assume that, in the undelayed environment, an efficient policy is known or can be easily learned, but the task may suffer from delays in practice and we thus want to take them into account. We present a novel algorithm, Delayed Imitation with Dataset Aggregation (DIDA), which builds upon imitation learning methods to learn how to act in a delayed environment from undelayed demonstrations. We provide a theoretical analysis of the approach that will guide the practical design of DIDA. These results are also of general interest in the delayed reinforcement learning literature by providing bounds on the performance between delayed and undelayed tasks, under smoothness conditions. We show empirically that DIDA obtains high performances with a remarkable sample efficiency on a variety of tasks, including robotic locomotion, classic control, and trading.