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Data-Based Predictive Control via Multistep Policy Gradient Reinforcement Learning
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AI
DOI：10.1109/TCYB.2021.3121078.png)
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Abstract
En 中文
In this article, a model-free predictive control algorithm for the real-time system is presented. The algorithm is data driven and is able to improve system performance based on multistep policy gradient reinforcement learning. By learning from the offline dataset and real-time data, the knowledge of system dynamics is avoided in algorithm design and application. Cooperative games of the multiplayer in time horizon are presented to model the predictive control as optimization problems of multiagent and guarantee the optimality of the predictive control policy. In order to implement the algorithm, neural networks are used to approximate the action-state value function and predictive control policy, respectively. The weights are determined by using the methods of weighted residual. Numerical results show the effectiveness of the proposed algorithm.
Keywords:
Predictive control
Real-time systems
Predictive models
Prediction algorithms
Reinforcement learning
Games
Cost function
Cooperative games
multistep reinforcement learning (RL)
policy gradient methods
predictive control
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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Cited Papers
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