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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 151175 of 1718 papers

TitleStatusHype
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy OptimizationCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
Efficient Multi-agent Reinforcement Learning by PlanningCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement LearningCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning ApproachCode1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario GenerationCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
Collaborative Visual NavigationCode1
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language ModelsCode1
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal ControlCode1
Collaborating with Humans without Human DataCode1
Inequity aversion improves cooperation in intertemporal social dilemmasCode1
ALMA: Hierarchical Learning for Composite Multi-Agent TasksCode1
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
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Benchmark Results

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