Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy

• URL: http://arxiv.org/abs/2206.05108v1
• Date: Fri, 10 Jun 2022 13:52:59 GMT
• Title: Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy
• Authors: Hongzhi Hua, Kaigui Wu and Guixuan Wen
• Abstract summary: We propose Deep Multi-Agent Hybrid Soft Actor-Critic (MAHSAC) to handle multi-agent problems with hybrid action spaces. This algorithm follows the centralized training but decentralized execution (CTDE) paradigm, and extend the Soft Actor-Critic algorithm (SAC) to handle hybrid action space problems. Our experiences are running on an easy multi-agent particle world with a continuous observation and discrete action space, along with some basic simulated physics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent deep reinforcement learning has been applied to address a variety of complex problems with either discrete or continuous action spaces and achieved great success. However, most real-world environments cannot be described by only discrete action spaces or only continuous action spaces. And there are few works having ever utilized deep reinforcement learning (drl) to multi-agent problems with hybrid action spaces. Therefore, we propose a novel algorithm: Deep Multi-Agent Hybrid Soft Actor-Critic (MAHSAC) to fill this gap. This algorithm follows the centralized training but decentralized execution (CTDE) paradigm, and extend the Soft Actor-Critic algorithm (SAC) to handle hybrid action space problems in Multi-Agent environments based on maximum entropy. Our experiences are running on an easy multi-agent particle world with a continuous observation and discrete action space, along with some basic simulated physics. The experimental results show that MAHSAC has good performance in training speed, stability, and anti-interference ability. At the same time, it outperforms existing independent deep hybrid learning method in cooperative scenarios and competitive scenarios.

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