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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 201250 of 1718 papers

TitleStatusHype
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution NetworksCode1
Multi-agent Reinforcement Learning in Sequential Social DilemmasCode1
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement LearningCode1
Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics ScenesCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement LearningCode1
Negative Update Intervals in Deep Multi-Agent Reinforcement LearningCode1
Neural Auto-Curricula in Two-Player Zero-Sum GamesCode1
Off-Policy Multi-Agent Decomposed Policy GradientsCode1
Neural Auto-CurriculaCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learningCode1
An Empirical Study on Google Research Football Multi-agent ScenariosCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement LearningCode1
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information CollaborationCode1
POGEMA: A Benchmark Platform for Cooperative Multi-Agent PathfindingCode1
Cooperative Policy Learning with Pre-trained Heterogeneous Observation RepresentationsCode1
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Regularized Softmax Deep Multi-Agent Q-LearningCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Resilient Consensus-based Multi-agent Reinforcement Learning with Function ApproximationCode1
Revisiting the Gumbel-Softmax in MADDPGCode1
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value FactorizationCode1
E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement LearningCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path FindingCode1
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement LearningCode1
Scalable Multi-Agent Model-Based Reinforcement LearningCode1
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal ControlCode1
Scaling Multi-Agent Reinforcement Learning with Selective Parameter SharingCode1
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language ModelsCode1
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement LearningCode1
Game-Theoretic Multiagent Reinforcement LearningCode1
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-LearningCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement LearningCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
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Benchmark Results

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