Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks

TL;DR

This paper introduces an adversarial training approach for multi-task reinforcement learning that enhances exploration and robustness in unseen tasks, without requiring manual tuning or domain knowledge.

Contribution

It proposes a novel adversarial training regime for MT-RL that improves agent robustness and exploration in dynamic, unpredictable environments.

Findings

Adversarial training improves exploration in MT-RL environments.

Agents trained with adversarial methods perform better on unseen tasks.

The approach operates without manual intervention or domain-specific knowledge.

Abstract

Advancements in reinforcement learning (RL) have been remarkable in recent years. However, the limitations of traditional training methods have become increasingly evident, particularly in meta-RL settings where agents face new, unseen tasks. Conventional training approaches are susceptible to failure in such situations as they need more robustness to adversity. Our proposed adversarial training regime for Multi-Task Reinforcement Learning (MT-RL) addresses the limitations of conventional training methods in RL, especially in meta-RL environments where the agent faces new tasks. The adversarial component challenges the agent, forcing it to improve its decision-making abilities in dynamic and unpredictable situations. This component operates without relying on manual intervention or domain-specific knowledge, making it a highly versatile solution. Experiments conducted in multiple MT-RL environments demonstrate that adversarial training leads to better exploration and a deeper understanding of the environment. The adversarial training regime for MT-RL presents a new perspective on training and development for RL agents and is a valuable contribution to the field.