Open AccessProceedings Article
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid,Mikayel Samvelyan,Christian Schroeder,Gregory Farquhar,Jakob Foerster,Shimon Whiteson +5 more
- pp 4292-4301
TLDR
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, and structurally enforce that the joint-action value is monotonic in the per- agent values, which allows tractable maximisation of the jointaction-value in off-policy learning.About:
This article is published in International Conference on Machine Learning.The article was published on 2018-07-03 and is currently open access. It has received 505 citations till now. The article focuses on the topics: Reinforcement learning & Monotonic function.read more
Citations
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Battlesnake challenge: A multi-agent reinforcement learning playground with human-in-the-loop
TL;DR: The results show that agents with the proposed HILL methods consistently outperform agents without HILL, and heuristics of reward manipulation had the best performance in the online competition.
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Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together.
TL;DR: This paper proposes a novel decentralized partially observable RL algorithm that uses a hierarchical structure to decompose the multi-objective task into unrelated ones and calculates a theoretical weight that makes each tasks reward has equal influence on the final RL value function.
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Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning.
TL;DR: In this article, the authors propose a robust local advantage (ROLA) actor-critic, which allows each agent to learn an individual action-value function as a local critic as well as ameliorate environment non-stationarity via a novel centralized training approach based on a centralized critic.
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Learning Complex Multi-Agent Policies in Presence of an Adversary.
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Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning.
TL;DR: A novel team RM based Bayesian MARL is proposed with three key contributions: a novel RM method to train cooperative agents as a team and obtain a team regret-based policy for that team; a novel method to de-compose the team regret to generate the policy for each agent for decentralized execution; and to further improve the perfor-mance, a differential particle filter network to get an accurate estimation of the state of each agent.
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