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Experience Replay for Optimal Control of Nonzero-Sum Game Systems With Unknown Dynamics
IEEE Transactions on Cybernetics
|November 4, 2015
Summary
This study introduces an approximate online equilibrium solution for N-player nonzero-sum (NZS) games with unknown dynamics. The method uses neural networks and experience replay for stable control and convergence in complex game systems.
Area of Science:
- Game Theory
- Control Systems
- Artificial Intelligence
Background:
- N-player nonzero-sum (NZS) games with unknown dynamics present significant control challenges.
- Traditional methods often require complete system knowledge or strong persistence of excitation conditions.
Purpose of the Study:
- To develop an approximate online equilibrium solution for N-player NZS game systems with unknown dynamics.
- To ensure stability and convergence of value functions in adaptive dynamic programming (ADP) control schemes.
Main Methods:
- A three-layer neural network (NN) model identifier is used to reconstruct unknown NZS game dynamics.
- Experience replay technique is employed to update identifier weights, relaxing excitation conditions.
- A single-network adaptive dynamic programming (ADP) algorithm with experience replay solves coupled nonlinear Hamilton-Jacobi (HJ) equations.
Main Results:
- The proposed method provides a feedback Nash equilibrium solution.
- A novel critic NN weights tuning law guarantees closed-loop system stability and value function convergence.
- Lyapunov-based stability analysis confirms uniform ultimate boundedness of the closed-loop system.
Conclusions:
- The developed approximate online equilibrium solution effectively addresses N-player NZS games with unknown dynamics.
- The integration of neural networks and experience replay offers a robust approach to adaptive control.
- Simulation examples validate the proposed control scheme's effectiveness.
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