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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 251275 of 1718 papers

TitleStatusHype
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Shared Experience Actor-Critic for Multi-Agent Reinforcement LearningCode1
Learning Individually Inferred Communication for Multi-Agent CooperationCode1
The Emergence of IndividualityCode1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Learning to Model Opponent LearningCode1
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive EnvironmentsCode1
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction LibraryCode1
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement LearningCode1
Who2com: Collaborative Perception via Learnable Handshake CommunicationCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
On the Robustness of Cooperative Multi-Agent Reinforcement LearningCode1
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal ControlCode1
"Other-Play" for Zero-Shot CoordinationCode1
Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal ControlCode1
Represented Value Function Approach for Large Scale Multi Agent Reinforcement LearningCode1
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement LearningCode1
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement LearningCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement LearningCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
The StarCraft Multi-Agent ChallengeCode1
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Benchmark Results

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