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 1071–1080 of 1718 papers

Title	Date	Tasks	Status
Trust Region Bounds for Decentralized PPO Under Non-stationarity	Jan 31, 2022	Multi-agent Reinforcement Learning	—Unverified
Generalization in Cooperative Multi-Agent Systems	Jan 31, 2022	Generalization BoundsMulti-agent Reinforcement Learning	—Unverified
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center	Jan 27, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Probe-Based Interventions for Modifying Agent Behavior	Jan 26, 2022	Decision MakingMulti-agent Reinforcement Learning	—Unverified
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning	Jan 26, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming	Jan 20, 2022	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available
Anytime PSRO for Two-Player Zero-Sum Games	Jan 19, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Interpretable Learned Emergent Communication for Human-Agent Teams	Jan 19, 2022	Multi-agent Reinforcement Learning	—Unverified
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal	Jan 18, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
Implementations that Matter in Cooperative Multi-Agent Reinforcement Learning	Jan 17, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified

Show:10 25 50

← PrevPage 108 of 172Next →

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