Multi-Agent Collaborative Target Search Based on the Multi-Agent Deep Deterministic Policy Gradient with Emotional Intrinsic Motivation

Author:

Zhang Xiaoping12ORCID,Zheng Yuanpeng1,Wang Li1,Abdulali Arsen2,Iida Fumiya2

Affiliation:

1. School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China

2. Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK

Abstract

Multi-agent collaborative target search is one of the main challenges in the multi-agent field, and deep reinforcement learning (DRL) is a good way to learn such a task. However, DRL always faces the problem of sparse reward, which to some extent reduces its efficiency in task learning. Introducing intrinsic motivation has proved to be a useful way to make the sparse reward in DRL. So, based on the multi-agent deep deterministic policy gradient (MADDPG) structure, a new MADDPG algorithm with the emotional intrinsic motivation name MADDPG-E is proposed in this paper for the multi-agent collaborative target search. In MADDPG-E, a new emotional intrinsic motivation module with three emotions, joy, sadness, and fear, is designed. The three emotions are defined by corresponding psychological knowledge to the multi-agent embodied situations in an environment. An emotional steady-state variable function H is then designed to help judge the goodness of the emotions. Based on H, an emotion-based intrinsic reward function is finally proposed. With the designed emotional intrinsic motivation module, the multi-agent system always tries to make itself joy, which means it always learns to search the target. To show the effectiveness of the proposed MADDPG-E algorithm, two kinds of simulation experiments with a determined initial position and random initial position, respectively, are carried out, and comparisons are performed with MADDPG as well as MADDPG-ICM (MADDPG with an intrinsic curiosity module). The results show that with the designed emotional intrinsic motivation module, MADDPG-E has a higher learning speed and better learning stability, and the advantage is more obvious when facing complex situations.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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