2018年3月30日 · QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations. We structurally enforce that the joint-action value is monotonic in the per-agent values, which allows tractable maximisation of the joint action-value in off-policy learning, and ...

本文介绍了 qmix，这是一种深度多智能体rl 方法，允许集中式学习分散式执行，并有效利用额外的状态信息。 QMIX 允许学习丰富的联合行动值函数，并可将其分解为每个智能体的行动值函数。

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning is a value-based method that can train decentralized policies in a centralized end-to-end fashion. QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations.

We evaluate QMIX on a range of unit micromanagement tasks built in StarCraft II1. (Vinyals et al.,2017). Our exper-iments show that QMIX outperforms IQL and VDN, both in terms of absolute performance and learning speed. In par-ticular, our method shows considerable performance gains on a task with heterogeneous agents. Moreover, our abla-

ElegantRL is an open-source massively parallel library for deep reinforcement learning (DRL) algorithms, implemented in PyTorch. We aim to provide a next-generation framework that leverage recent techniques, e.g., massively parallel simulations, ensemble methods, population-based training, and showcase exciting scientific discoveries.

本文介绍了qmix，这是一种深度的多主体rl方法，它允许在集中设置中端到端学习分散策略，并有效利用额外的状态信息。qmix允许学习丰富的 联合动作值函数 ，该函数允许将易分解的分解分解为每个代理的

Our solution is QMIX, a novel value-based method that can train decen-tralised policies in a centralised end-to-end fash-ion. QMIX employs a network that estimates joint action-values as a complex non-linear combina-tion of per-agent values that condition only on lo-cal observations.

2024年6月20日 · In this paper, we propose an enhancement to QMIX by incorporating an additional local Q-value learning method within the maximum entropy RL framework. Our approach constrains the local Q-value estimates to maintain the correct ordering of all actions.

We evaluate QMIX on a challenging set of StarCraft II micromanagement tasks, and show that QMIX significantly outperforms existing value-based multi-agent reinforcement learning methods.

2024年8月21日 · 基于MADRL的单调价值函数分解（Monotonic Value Function Factorisation for Deep Multi- Agent Reinforcement Learning） QMIX 是一种用于 多智能体强化学习 的 算法，特别适用于需要协作的多智能体环境，如分布式控制、团队作战等场景。 QMIX 算法由 Rashid 等人在 2018 年提出，其核心思想是通过一种混合网络（Mixing Network）来对各个智能体的局部 Q 值进行非线性组合，从而得到全局 Q 值。 算法原文： Monotonic Value Function Factorisation for …