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
More filters
Posted Content
Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning.
TL;DR: Soft Team Actor-Critic (STAC) is introduced as a solution to the team's coordination problem that does not require any prior domain knowledge and reaches near-optimal coordinated strategies both in perfectly observable and partially observable games, where previous deep RL algorithms fail to reach optimal coordinated behaviors.
Journal ArticleDOI
Throughput Optimization for Grant-Free Multiple Access With Multiagent Deep Reinforcement Learning
TL;DR: A deep reinforcement learning (DRL)-based pilot sequence selection scheme for GFMA systems to mitigate potential pilot sequence collisions and the capability of the proposed scheme to support IoT devices with specific throughput requirements is demonstrated.
Posted Content
From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
Weixun Wang,Tianpei Yang,Yong Liu,Jianye Hao,Xiaotian Hao,Yujing Hu,Yingfeng Chen,Changjie Fan,Yang Gao +8 more
TL;DR: In this article, the authors design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents.
Posted Content
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net
TL;DR: In this article, a macro-action-based decentralized multi-agent double deep recurrent Q-net (MacDec-MADDRQN) is proposed to solve the problem of high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control.
Proceedings ArticleDOI
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward
TL;DR: DecDecomposed Multi-Agent Deep Deterministic Policy Gradient (DE-MADDPG) as discussed by the authors is a cooperative multi-agent reinforcement learning framework that simultaneously learns to maximize the global and local rewards.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Posted Content
Empirical evaluation of gated recurrent neural networks on sequence modeling
TL;DR: These advanced recurrent units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU), are found to be comparable to LSTM.
Deep reinforcement learning with double Q-learning
TL;DR: In this article, the authors show that the DQN algorithm suffers from substantial overestimation in some games in the Atari 2600 domain, and they propose a specific adaptation to the algorithm and show that this algorithm not only reduces the observed overestimations, but also leads to much better performance on several games.
Journal ArticleDOI
Learning from delayed rewards
TL;DR: The invention relates to a circuit for use in a receiver which can receive two-tone/stereo signals which is intended to make a choice between mono or stereo reproduction of signal A or of signal B and vice versa.