Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning
This addresses the challenge of decentralized multi-robot pursuit for applications like surveillance or search-and-rescue, but it is incremental as it builds on existing methods.
The paper tackles the problem of coordinating multiple robots to hunt an evader in a decentralized manner using local observations, by proposing a hybrid algorithm that combines reinforcement learning with artificial potential fields, resulting in improved performance over baseline methods in simulations and feasibility in real-world robot experiments.
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.