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 51–100 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
Offline Multi-agent Reinforcement Learning via Score Decomposition—0
Learning Power Control Protocol for In-Factory 6G Subnetworks—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
Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis—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
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal ControlCode1
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
Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound—0
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems—0
LERO: LLM-driven Evolutionary framework with Hybrid Rewards and Enhanced Observation for Multi-Agent Reinforcement Learning—0
Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control—0
Abstracting Geo-specific Terrains to Scale Up Reinforcement Learning—0
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm—0
Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization—0
A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference—0
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Benchmark Results

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