A model-based reinforcement learning method based on conditional generative adversarial networks

doi:10.1016/j.patrec.2021.08.019

A model-based reinforcement learning method based on conditional generative adversarial networks

2021-12-01

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DOI：10.1016/j.patrec.2021.08.019

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Deep reinforcement learning (DRL) integrates the advantages of the perception of deep learning and en-ables reinforcement learning scale to problems with high dimensional state and action spaces that were previously intractable. The success of DRL primarily relies on the high level representation ability of deep learning. To obtain a good performed representation model, excessive training samples and training time are necessary. However, collecting a large number of samples in real world is extremely expensive and time consuming. To mitigate the sample inefficiency problem, we propose a novel model-based reinforce-ment learning method by combining conditional generative adversarial networks (CGAN-MbRL) with the state-of-the-art policy learning method. The proposed CGAN-MbRL can directly deal with the high dimen-sional state, and mitigate the problem of sample inefficiency to some extent. Finally, the effectiveness of the proposed method is demonstrated through the illustrative data and the RL benchmark. (c) 2021 Elsevier B.V. All rights reserved.

Keywords:

Generative adversarial networks

Model-based reinforcement learning

Policy learning

Policy learning Transition model learning

A model-based reinforcement learning method based on conditional generative adversarial networks

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