SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1451–1475 of 1718 papers

TitleStatusHype
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement LearningCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning with Partial Observations—0
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement LearningCode0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response—0
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks—0
Learning to Communicate Using Counterfactual Reasoning—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Shared Experience Actor-Critic for Multi-Agent Reinforcement LearningCode1
Multi-Agent Informational Learning Processes—0
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward—0
Multi-Agent Reinforcement Learning in Stochastic Networked SystemsCode0
Learning Individually Inferred Communication for Multi-Agent CooperationCode1
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation—0
The Emergence of IndividualityCode1
Skill Discovery of Coordination in Multi-agent Reinforcement Learning—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Learning to Model Opponent LearningCode1
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization—0
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement LearningCode0
Show:102550
← PrevPage 59 of 69Next →
All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified