A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space

• URL: http://arxiv.org/abs/2208.14447v1
• Date: Tue, 30 Aug 2022 07:40:15 GMT
• Title: A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space
• Authors: Hongzhi Hua, Guixuan Wen, Kaigui Wu
• Abstract summary: We propose two algorithms: deep multi-agent hybrid soft actor-critic (MAHSAC) and multi-agent hybrid deep deterministic policy gradients (MAHDDPG) Our experiences are running on multi-agent particle environment which is an easy multi-agent particle world, along with some basic simulated physics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The research of extending deep reinforcement learning (drl) to multi-agent field has solved many complicated problems and made great achievements. However, almost all these studies only focus on discrete or continuous action space and there are few works having ever used multi-agent deep reinforcement learning to real-world environment problems which mostly have a hybrid action space. Therefore, in this paper, we propose two algorithms: deep multi-agent hybrid soft actor-critic (MAHSAC) and multi-agent hybrid deep deterministic policy gradients (MAHDDPG) to fill this gap. This two algorithms follow the centralized training and decentralized execution (CTDE) paradigm and could handle hybrid action space problems. Our experiences are running on multi-agent particle environment which is an easy multi-agent particle world, along with some basic simulated physics. The experimental results show that these algorithms have good performances.

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