Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents

TL;DR

This paper introduces an eco-system of reinforcement learning agents that enhances generalization to new environments and mitigates catastrophic forgetting by leveraging collective adaptive capabilities.

Contribution

It proposes a novel eco-system approach that combines multiple agents to improve adaptability and prevent forgetting in reinforcement learning tasks.

Findings

Enhanced performance in unseen environments

Reduced catastrophic forgetting during retraining

Demonstrated effectiveness across multiple RL benchmarks

Abstract

Adapting a Reinforcement Learning (RL) agent to an unseen environment is a difficult task due to typical over-fitting on the training environment. RL agents are often capable of solving environments very close to the trained environment, but when environments become substantially different, their performance quickly drops. When agents are retrained on new environments, a second issue arises: there is a risk of catastrophic forgetting, where the performance on previously seen environments is seriously hampered. This paper proposes a novel approach that exploits an eco-system of agents to address both concerns. Hereby, the (limited) adaptive power of individual agents is harvested to build a highly adaptive eco-system.