AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

• URL: http://arxiv.org/abs/2107.02729v2
• Date: Wed, 7 Jul 2021 07:21:38 GMT
• Title: AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
• Authors: Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang
• Abstract summary: We propose a principled framework for adaptive RL, called AdaRL, that adapts reliably to changes across domains. We show that AdaRL can adapt the policy with only a few samples without further policy optimization in the target domain. We illustrate the efficacy of AdaRL through a series of experiments that allow for changes in different components of Cartpole and Atari games.
• Score: 18.269412736181852
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Most approaches in reinforcement learning (RL) are data-hungry and specific to fixed environments. In this paper, we propose a principled framework for adaptive RL, called AdaRL, that adapts reliably to changes across domains. Specifically, we construct a generative environment model for the structural relationships among variables in the system and embed the changes in a compact way, which provides a clear and interpretable picture for locating what and where the changes are and how to adapt. Based on the environment model, we characterize a minimal set of representations, including both domain-specific factors and domain-shared state representations, that suffice for reliable and low-cost transfer. Moreover, we show that by explicitly leveraging a compact representation to encode changes, we can adapt the policy with only a few samples without further policy optimization in the target domain. We illustrate the efficacy of AdaRL through a series of experiments that allow for changes in different components of Cartpole and Atari games.

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