Delay-Aware Model-Based Reinforcement Learning for Continuous Control

• URL: http://arxiv.org/abs/2005.05440v1
• Date: Mon, 11 May 2020 21:13:37 GMT
• Title: Delay-Aware Model-Based Reinforcement Learning for Continuous Control
• Authors: Baiming Chen, Mengdi Xu, Liang Li, Ding Zhao
• Abstract summary: 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. We develop a delay-aware model-based reinforcement learning framework.
• Score: 22.92068095246967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 off-policy model-free reinforcement learning methods. Codes available at: https://github.com/baimingc/dambrl.

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