# Model-based reinforcement learning in differential graphical games

## TL;DR

This paper integrates differential game theory with actor-critic-identifier reinforcement learning to develop cooperative control strategies for multi-agent formation tracking with uncertain nonlinear dynamics, demonstrated through simulations.

## Contribution

It introduces a novel model-based reinforcement learning approach combining differential game theory for formation control of multi-agent systems with uncertain dynamics.

## Key findings

- Effective formation tracking achieved in simulations
- Robustness to uncertain nonlinear dynamics demonstrated
- Communication topology with spanning tree facilitates control

## Abstract

This paper seeks to combine differential game theory with the actor-critic-identifier architecture to determine forward-in-time, approximate optimal controllers for formation tracking in multi-agent systems, where the agents have uncertain heterogeneous nonlinear dynamics. A continuous control strategy is proposed, using communication feedback from extended neighbors on a communication topology that has a spanning tree. A model-based reinforcement learning technique is developed to cooperatively control a group of agents to track a trajectory in a desired formation. Simulation results are presented to demonstrate the performance of the developed technique.