Deep Reinforcement Learning Approach for Flocking Control of Multi-agents
Han Zhang, Jin Cheng · 2021
Flocking behaviors learning with multi-agents deep deterministic policy gradient algorithm is addressed in this paper. Different from the non-intelligent algorithm, agents constantly update strategies by learning the experience of random exploration, so as to obtain the optimal action. This algorithm makes the multi-agents system have both the decision ability of reinforcement learning and the data processing ability of deep learning. An artificial potential energy function is designed to evaluate the reward of aggregation posture. Actor-Critic framework is adopted to improve the parameter updating mechanism of neural network. Simulations are implemented to illustrate the flocking control performance of the learning behavor. Results show that the flocking behavor of multi-agents are satisfied as desired.