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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 226250 of 1718 papers

TitleStatusHype
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit DesignCode1
An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative TasksCode1
Reinforced Prompt Personalization for Recommendation with Large Language ModelsCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
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