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arXiv · 1907.05855

DisCoRL: Continual Reinforcement Learning via Policy Distillation

René Traoré·Hugo Caselles-Dupré·Timothée Lesort·Te Sun·Guanghang Cai·Natalia Díaz-Rodríguez·David Filliat
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Abstract

In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of those policies applying without an external signal. In the case of continual reinforcement learning a third challenge arises: learning tasks sequentially without forgetting the previous ones. In this paper, we tackle these challenges by proposing DisCoRL, an approach combining state representation learning and policy distillation. We experiment on a sequence of three simulated 2D navigation tasks with a 3 wheel omni-directional robot. Moreover, we tested our approach's robustness by transferring the final policy into a real life setting. The policy can solve all tasks and automatically infer which one to run.

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René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Guanghang Cai, Natalia Díaz-Rodríguez, David Filliat. 2019-07-11. DisCoRL: Continual Reinforcement Learning via Policy Distillation. https://arxiv.org/abs/1907.05855

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