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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 251300 of 1718 papers

TitleStatusHype
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online AdvertisingCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication MethodCode1
Multi-Agent Path Finding with Prioritized Communication LearningCode1
Multi-Agent Reinforcement Learning for Adaptive Mesh RefinementCode1
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution NetworksCode1
More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy FactorizationCode1
Cooperative Policy Learning with Pre-trained Heterogeneous Observation RepresentationsCode1
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive EnvironmentsCode1
Collaborative Visual NavigationCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Multiagent Reinforcement Learning Based on Fusion-Multiactor-Attention-Critic for Multiple-Unmanned-Aerial-Vehicle Navigation ControlCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement LearningCode1
Multi-Agent Collaboration via Reward Attribution DecompositionCode1
Multi Agent Reinforcement Learning for Sequential Satellite Assignment ProblemsCode1
Inequity aversion improves cooperation in intertemporal social dilemmasCode1
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level OptimizationCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Collaborating with Humans without Human DataCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
N-Agent Ad Hoc TeamworkCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Neural Auto-CurriculaCode1
Multi-Agent Constrained Policy OptimisationCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Multi-agent Reinforcement Learning in Sequential Social DilemmasCode1
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit DesignCode1
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement LearningCode1
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?Code0
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement LearningCode0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
Mediated Multi-Agent Reinforcement LearningCode0
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global StateCode0
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic ScenarioCode0
MDPGT: Momentum-based Decentralized Policy Gradient TrackingCode0
An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement LearningCode0
MAVEN: Multi-Agent Variational ExplorationCode0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
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Benchmark Results

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