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

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Showing 1051–1060 of 1718 papers

Title	Date	Tasks	Status
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning	Oct 14, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning	Oct 13, 2022	Federated LearningMulti-agent Reinforcement Learning	—Unverified
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations	Oct 13, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning	Oct 12, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning	Oct 11, 2022	Anomaly DetectionMulti-agent Reinforcement Learning	CodeCode Available
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient	Oct 10, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Multi-agent Deep Covering Skill Discovery	Oct 7, 2022	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified
Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios	Oct 5, 2022	Autonomous VehiclesMulti-agent Reinforcement Learning	—Unverified
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning	Oct 4, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games	Oct 3, 2022	Multi-agent Reinforcement Learning	—Unverified

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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