Emotion-Based Heterogeneous Multi-agent Reinforcement Learning with Sparse Reward

In reinforcement learning, the convergence speed and efficiency of the agent are greatly reduced due to its inability to acquire effective experience in an sparse reward distribution environment. Aiming at this kind of sparse reward problem, a method of emotion-based heterogeneous multi-agent reinforcement learning with sparse reward is proposed in this paper. Firstly, the emotion model based on personality is established to provide incentive mechanism for multiple heterogeneous agents as an effective supplement to external rewards. Then, based on this mechanism, a deep deterministic strategy gradient reinforcement learning algorithm based on intrinsic emotional incentive mechanism under sparse rewards is proposed to accelerate the convergence speed of agents. Finally, multi-robot pursuit is used as a simulation experiment platform to construct sparse reward scenarios with different difficulty levels, and the effectiveness and superiority of the proposed method in pursuit success rate and convergence speed are verified. © 2021, Science Press. All right reserved.