Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning

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Model-based reinforcement learning (RL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environment's dynamics. However, learning a global model that can generalize across different dynamics is a challenging task. To tackle this problem, we decompose the task of learning a global dynamics model into two stages: (a) learning a context latent vector that captures the local dynamics, then (b) predicting the next state conditioned on it. In order to encode dynamics-specific information into the context latent vector, we introduce a novel loss function that encourages the context latent vector to be useful for predicting both forward and backward dynamics. The proposed method achieves superior generalization ability across various simulated robotics and control tasks, compared to existing RL schemes.
Publisher
International Conference on Machine Learning
Issue Date
2020-07-15
Language
English
Citation

Thirty-seventh International Conference on Machine Learning, ICML 2020, pp.5713 - 5722

ISSN
2640-3498
URI
http://hdl.handle.net/10203/278491
Appears in Collection
RIMS Conference Papers
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