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Adapting a Reinforcement Learning (RL) agent to an unseen environment is adifficult task due to typical over-fitting on the training environment. RLagents are often capable of solving environments very close to the trainedenvironment, but when environments become substantially different, theirperformance quickly drops. When agents are retrained on new environments, asecond issue arises: there is a risk of catastrophic forgetting, where theperformance on previously seen environments is seriously hampered. This paperproposes a novel approach that exploits an eco-system of agents to address bothconcerns. Hereby, the (limited) adaptive power of individual agents isharvested to build a highly adaptive eco-system.

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