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Delay-aware model-based reinforcement learning for continuous control
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DOI：10.1016/j.neucom.2021.04.015.png)
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Abstract
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Action delays degrade the performance of reinforcement learning in many real-world systems. This paper proposes a formal definition of delay-aware Markov Decision Process and proves it can be transformed into standard MDP with augmented states using the Markov reward process. We develop a delay-aware model-based reinforcement learning framework that can incorporate the multi-step delay into the learned system models without learning effort. Experiments with the Gym and MuJoCo platforms show that the proposed delay-aware model-based algorithm is more efficient in training and transferable between systems with various durations of delay compared with state-of-the-art model-free reinforce-ment learning methods. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Model-based reinforcement learning
Markov decision process
Continuous control
Delayed system
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6.5
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