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Improving generalization in reinforcement learning through forked agents

2022-12-13 09:47:28
Olivier Moulin, Vincent Francois-Lavet, Mark Hoogendoorn
arXiv_AI
       
Abstract

An eco-system of agents each having their own policy with some, but limited, generalizability has proven to be a reliable approach to increase generalization across procedurally generated environments. In such an approach, new agents are regularly added to the eco-system when encountering a new environment that is outside of the scope of the eco-system. The speed of adaptation and general effectiveness of the eco-system approach highly depends on the initialization of new agents. In this paper we propose different techniques for such initialization and study their impact. We then rework the ecosystem setup to use forked agents which brings better results than the initial eco-system approach with a drastically reduced number of training cycles.

Abstract (translated)

URL

https://arxiv.org/abs/2212.06451

PDF

https://arxiv.org/pdf/2212.06451.pdf

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