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The research of extending deep reinforcement learning (drl) to multi-agentfield has solved many complicated problems and made great achievements.However, almost all these studies only focus on discrete or continuous actionspace and there are few works having ever used multi-agent deep reinforcementlearning to real-world environment problems which mostly have a hybrid actionspace. Therefore, in this paper, we propose two algorithms: deep multi-agenthybrid soft actor-critic (MAHSAC) and multi-agent hybrid deep deterministicpolicy gradients (MAHDDPG) to fill this gap. This two algorithms follow thecentralized training and decentralized execution (CTDE) paradigm and couldhandle hybrid action space problems. Our experiences are running on multi-agentparticle environment which is an easy multi-agent particle world, along withsome basic simulated physics. The experimental results show that thesealgorithms have good performances.

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