We present a multi-agent reinforcement learning approach to solve a pursuit-evasion game between two players with car-like dynamics and sensing limitations.
We develop a curriculum for an existing multi-agent deterministic policy gradient algorithm to simultaneously obtain strategies for both players, and deploy the learned strategies on real robots moving as fast as 2 m/s in indoor environments.
Through experiments we show that the learned strategies improve over existing baselines by up to 30% in terms of capture rate for the pursuer.
The learned evader model has up to 5% better escape rate over the baselines even against our competitive pursuer model.
We also present experiment results which show how the pursuit-evasion game and its results evolve as the player dynamics and sensor constraints are varied.
Finally, we deploy learned policies on physical robots for a game between the F1TENTH and JetRacer platforms and show that the learned strategies can be executed on real-robots.
