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Novel Parallel Formulation for Iterative Reinforcement Learning Control
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AI
DOI：10.1109/TSMC.2024.3428482.png)
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Abstract
En 中文
Parallelization is widely employed to improve the exploration ability of controllers. However, it is rare to provide a lightweight scheme for reducing homogeneous policies with theoretical guarantees. This article is concerned with a novel parallel scheme for solving optimal control problems. In brief, we design a novel global indicator that inherits the theoretical guarantees of a class of iterative reinforcement learning algorithms. By generating a tentative function, the global indicator can guide and communicate with parallel controllers to accelerate the learning process. Using two typical exploration policies, the novel parallel scheme can rapidly compress the neighborhood of the optimal cost function. Besides, two parallel algorithms based on value iteration and Q-learning are established to improve the data efficiency through different extensions. Finally, two benchmark problems are presented to demonstrate the learning effectiveness of the novel parallel scheme.
Keywords:
Q-learning
Adaptive critic
discrete-time systems
nonlinear control
nonlinear control
parallel learning
parallel learning
nonlinear control
reinforcement learning (RL)
reinforcement learning (RL)
value iteration (VI)
value iteration (VI)
reinforcement learning (RL)
value iteration (VI)
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W