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Environments for Lifelong Reinforcement Learning

2018-11-26Code Available0· sign in to hype

Khimya Khetarpal, Shagun Sodhani, Sarath Chandar, Doina Precup

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Abstract

To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned. In this paper, we discuss the desired characteristics of environments that can support the training and evaluation of lifelong reinforcement learning agents, review existing environments from this perspective, and propose recommendations for devising suitable environments in the future.

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