  It is of great challenge, though promising, to coordinate collective robots
for hunting an evader in a decentralized manner purely in light of local
observations. In this paper, this challenge is addressed by a novel hybrid
cooperative pursuit algorithm that combines reinforcement learning with the
artificial potential field method. In the proposed algorithm, decentralized
deep reinforcement learning is employed to learn cooperative pursuit policies
that are adaptive to dynamic environments. The artificial potential field
method is integrated into the learning process as predefined rules to improve
the data efficiency and generalization ability. It is shown by numerical
simulations that the proposed hybrid design outperforms the pursuit policies
either learned from vanilla reinforcement learning or designed by the potential
field method. Furthermore, experiments are conducted by transferring the
learned pursuit policies into real-world mobile robots. Experimental results
demonstrate the feasibility and potential of the proposed algorithm in learning
multiple cooperative pursuit strategies.
