Off-Policy Model-Free Learning for Multi-Player Non-Zero-Sum Games With Constrained Inputs

Qiao, Junfei; Wang, Ding; Qiao, Junfei; Li, Menghua

doi:10.1109/tcsi.2022.3221274

IEEE Trans. Circuits Syst. I

2023

DOI: 10.1109/tcsi.2022.3221274

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Off-Policy Model-Free Learning for Multi-Player Non-Zero-Sum Games With Constrained Inputs

Junfei Qiao

Ding Wang

Junfei Qiao

et al.

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Cited by 11 publications

References 57 publications

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Safe tracking in games: Achieving optimal control with unknown dynamics and constraints

Cui,

Chen,

Wang

et al. 2024

Asian Journal of Control

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This paper introduces mix‐zero‐sum differential (MZSD) game theory to address multi‐player tracking systems, offering a better understanding of the coexistence of cooperation and competition among players. Within this framework, we present an optimal safety tracking control (OSTC) method, which incorporates a control barrier function (CBF) into the value function to ensure that the tracking error remains within a specified range, thus guaranteeing safety while achieving optimization. Simultaneously, to eliminate the need for system dynamics, we propose a novel approach leveraging off‐policy integral reinforcement learning (IRL) technology to obtain the Nash equilibrium solution of the MZSD games. We establish a unique critics–actors neural network (NN) structure that updates concurrently. Furthermore, we analyze stability and convergence using the Lyapunov method. We conduct two simulations to demonstrate the effectiveness of the proposed algorithm.

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Safe tracking in games: Achieving optimal control with unknown dynamics and constraints

Cui,

Chen,

Wang

et al. 2024

Asian Journal of Control

View full text Add to dashboard Cite

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Discrete‐Time Optimal Control of State‐Constrained Nonlinear Systems Using Approximate Dynamic Programming

Song,

Gong,

Zhu

et al. 2024

Intl J Robust & Nonlinear

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This article investigates the optimal control problem (OCP) for a class of discrete‐time nonlinear systems with state constraints. First, to overcome the challenge caused by the constraints, the original constrained OCP is transformed into an unconstrained OCP by utilizing the system transformation technique. Second, a new cost function is designed to alleviate the effect of system transformation on the optimality of the original system. Further, a novel off‐policy deterministic approximate dynamic programming (ADP) scheme is developed to obtain a near‐optimal solution for the transformed OCP. Compared to existing off‐policy deterministic ADP schemes, the developed scheme relaxes the requirement on the learning data and saves computing resources from the perspective of training neural networks. Third, considering approximation errors, we analyze the convergence and stability of the developed ADP scheme. Finally, the developed ADP with the designed cost function is tested in two numerical cases, and simulation results confirm its effectiveness.

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Online solving Nash equilibrium solution of N-player nonzero-sum differential games via recursive least squares

Song,

Yang

2023

Soft Comput

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Off-Policy Model-Free Learning for Multi-Player Non-Zero-Sum Games With Constrained Inputs

Cited by 11 publications

References 57 publications

Safe tracking in games: Achieving optimal control with unknown dynamics and constraints

Safe tracking in games: Achieving optimal control with unknown dynamics and constraints

Discrete‐Time Optimal Control of State‐Constrained Nonlinear Systems Using Approximate Dynamic Programming

Online solving Nash equilibrium solution of N-player nonzero-sum differential games via recursive least squares

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