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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 301325 of 1718 papers

TitleStatusHype
Multi-Agent Common Knowledge Reinforcement LearningCode0
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement LearningCode0
Centralized control for multi-agent RL in a complex Real-Time-Strategy gameCode0
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement LearningCode0
Modelling crypto markets by multi-agent reinforcement learningCode0
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement LearningCode0
Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation StudyCode0
Multi-Agent Congestion Cost Minimization With Linear Function ApproximationsCode0
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement LearningCode0
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?Code0
Mediated Multi-Agent Reinforcement LearningCode0
Carbon Market Simulation with Adaptive Mechanism DesignCode0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive PrincipalCode0
A New Formalism, Method and Open Issues for Zero-Shot CoordinationCode0
Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?Code0
A collaboration of multi-agent model using an interactive interfaceCode0
MAVEN: Multi-Agent Variational ExplorationCode0
Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and qualityCode0
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective IntelligenceCode0
MAHTM: A Multi-Agent Framework for Hierarchical Transactive MicrogridsCode0
A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement LearningCode0
MAC-PO: Multi-Agent Experience Replay via Collective Priority OptimizationCode0
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement LearningCode0
Logic-based Reward Shaping 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