This paper investigates reinforcement learning algorithms for discrete-time stochastic multi-agent graphical games with multiplicative noise. The Bellman optimality equation for stochastic multi-agent graphical games ...
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This paper investigates reinforcement learning algorithms for discrete-time stochastic multi-agent graphical games with multiplicative noise. The Bellman optimality equation for stochastic multi-agent graphical games is obtained by using the optimality principle. A Nash equilibrium can be reached when each agent executes a strategy in terms of Bellman optimality equation. To circumvent the difficulty of solving the coupled Bellman equation, a value iteration heuristic dynamic programming (hdp) algorithm is designed and its convergence is shown. To solve multi-agent graphical games online, the hdp algorithm based on the actor-critic framework is designed to approximate Nash equilibrium solutions. The effectiveness of the algorithm is verified by two numerical simulation examples.
This paper considers a discrete time stochastic multi-agent graphical games problem with multiplicative noise based on reinforcement learning. The Bellman optimality equations for multi-agent graphical games are obtai...
详细信息
ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
This paper considers a discrete time stochastic multi-agent graphical games problem with multiplicative noise based on reinforcement learning. The Bellman optimality equations for multi-agent graphical games are obtained by using the optimality principle. Through the stability analysis, it is proved that the solutions of the equations converge to Nash equilibrium. Since the coupled Bellman equation is difficult to solve, the value iteration heuristic dynamic programming(hdp) algorithm is *** solve multi-agent graphical games online, the hdp algorithm based on the actor-critic framework is designed to find the approximate solutions, and the effectiveness of the algorithm is verified by a simulation example.
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