A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space. (arXiv:2208.14447v1 [cs.LG])

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.

Source: https://arxiv.org/abs/2208.14447


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