论文标题
通过混合动作空间进一步探索深度多代理的增强学习
A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space
论文作者
论文摘要
将深度强化学习（DRL）扩展到多代理领域的研究已经解决了许多复杂的问题，并取得了巨大的成就。但是，几乎所有这些研究都只关注离散或连续的动作空间，而且很少有曾经使用多代理深入的强化学习来实现现实世界中的环境问题，而这些环境问题主要具有混合动作空间。因此，在本文中，我们提出了两种算法：深层混合软性角色批评（MAHSAC）和多代理混合杂种深层确定性策略梯度（MAHDDPG），以填补这一空白。这两种算法遵循集中式培训和分散执行（CTDE）范式，并且可以解决混合动作空间问题。我们的经验是在多代理粒子环境上运行的，这是一个简单的多代理粒子世界，以及一些基本的模拟物理。实验结果表明，这些算法具有良好的性能。
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.
