Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1661–1670 of 1718 papers

Title	Date	Tasks	Status	Hype
Actor-Attention-Critic for Multi-Agent Reinforcement Learning	Oct 5, 2018	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning	Sep 29, 2018	ManagementMulti-agent Reinforcement Learning	CodeCode Available	0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs	Sep 27, 2018	Meta-LearningMulti-agent Reinforcement Learning	—Unverified	0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas	Sep 26, 2018	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning	Sep 20, 2018	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning	Sep 19, 2018	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning	Sep 17, 2018	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning	Sep 13, 2018	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Coordination-driven learning in multi-agent problem spaces	Sep 13, 2018	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning	Sep 13, 2018	Autonomous VehiclesEfficient Exploration	CodeCode Available	0

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All datasets ParticleEnvs Cooperative Communication SMAC-Exp UAV Logistics

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MATD3	final agent reward	-14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRIMA	Median Win Rate	15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Fusion-Multi-Actor-Attention-Critic	Average Reward	39	—	Unverified