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

Showing 276300 of 1718 papers

TitleStatusHype
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning ApproachCode1
Fully Decentralized Multi-Agent Reinforcement Learning with Networked AgentsCode1
Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal ControlCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario GenerationCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language ModelsCode1
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARLCode1
Interaction Pattern Disentangling for Multi-Agent Reinforcement LearningCode1
Mean Field Multi-Agent Reinforcement LearningCode1
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape EstimationCode1
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?Code1
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework0
Coding for Distributed Multi-Agent Reinforcement Learning0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning0
Anytime PSRO for Two-Player Zero-Sum Games0
AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning0
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach0
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering0
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks0
Correcting Experience Replay for Multi-Agent Communication0
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning0
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified