  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. Our code and supplementary material including videos from
experiments are available at https: //gonultasbu.github.io/pursuit-evasion/.
