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 351–375 of 1718 papers

TitleStatusHype
Safe Bottom-Up Flexibility Provision from Distributed Energy Resources—0
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
Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis—0
QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?—0
Multi-Agent Reinforcement Learning for Decentralized Reservoir Management via Murmuration Intelligence—0
Multi-Agent Reinforcement Learning for Greenhouse Gas Offset Credit Markets—0
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning—0
Belief States for Cooperative Multi-Agent Reinforcement Learning under Partial Observability—0
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control—0
Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement LearningCode0
Attention-Augmented Inverse Reinforcement Learning with Graph Convolutions for Multi-Agent Task Allocation—0
HypRL: Reinforcement Learning of Control Policies for Hyperproperties—0
OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning—0
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning—0
Markov Potential Game Construction and Multi-Agent Reinforcement Learning with Applications to Autonomous Driving—0
Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games—0
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems—0
Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound—0
Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control—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