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Discrete-Time Nonlinear Optimal Control Using Multi-Step Reinforcement Learning
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AI
DOI：10.1109/TCSII.2023.3343375.png)
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Abstract
En 中文
This brief solves the optimal control problem of discrete-time nonlinear systems by proposing a multi-step reinforcement learning (RL) algorithm. The proposed multi-step RL algorithm is established based on the discrete-time optimal Bellman equation, which takes advantage of policy iteration (PI) and value iteration (VI). Benefiting from the multi-step integration mechanism, the algorithm is accelerated. The convergence of multi-step RL is proved by mathematical induction. For real-world implementation purposes, neural network (NN) and Actor-Critic architecture are introduced to approximate the iterative value functions and control policies. A numerical simulation of Chua's circuit illustrates the effectiveness of the proposed algorithm.
Keywords:
Convergence
Optimal control
Heuristic algorithms
Mathematical models
Approximation algorithms
Reinforcement learning
Nonlinear systems
optimal bellman equation
actor-critic architecture
optimal control
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