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

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Showing 51–75 of 1718 papers

TitleStatusHype
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning—0
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
Multi-source Plume Tracing via Multi-Agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue—0
JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 MinutesCode1
Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL—0
Learning Power Control Protocol for In-Factory 6G Subnetworks—0
Offline Multi-agent Reinforcement Learning via Score Decomposition—0
CCL: Collaborative Curriculum Learning for Sparse-Reward Multi-Agent Reinforcement Learning via Co-evolutionary Task Evolution—0
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial ExplorationCode1
Adaptive and Robust DBSCAN with Multi-agent Reinforcement LearningCode0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning—0
Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation—0
Interpretable Emergent Language Using Inter-Agent TransformersCode0
Securing 5G and Beyond-Enabled UAV Networks: Resilience Through Multiagent Learning and Transformers Detection—0
Emergence of Roles in Robotic Teams with Model Sharing and Limited Communication—0
Safe and Efficient CAV Lane Changing using Decentralised Safety Shields—0
Safe Bottom-Up Flexibility Provision from Distributed Energy Resources—0
Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A SurveyCode2
Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL—0
Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey—0
Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning—0
Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning—0
QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?—0
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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