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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 301350 of 1718 papers

TitleStatusHype
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement LearningCode0
Smart Traffic Signals: Comparing MARL and Fixed-Time StrategiesCode0
Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular NetworksCode0
Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamicsCode0
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement LearningCode0
Centralized control for multi-agent RL in a complex Real-Time-Strategy gameCode0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile CommunicationsCode0
Multi-Agent Reinforcement Learning: A Report on Challenges and ApproachesCode0
Multi-Agent Reinforcement Learning for Visibility-based Persistent MonitoringCode0
Carbon Market Simulation with Adaptive Mechanism DesignCode0
Multi-Agent Quantum Reinforcement Learning using Evolutionary OptimizationCode0
Multi-Agent Reinforcement Learning with Focal Diversity OptimizationCode0
Multi-Agent Advisor Q-LearningCode0
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
Multi-Agent Common Knowledge Reinforcement LearningCode0
Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and qualityCode0
Multi-Agent Congestion Cost Minimization With Linear Function ApproximationsCode0
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
A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement LearningCode0
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive PrincipalCode0
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement LearningCode0
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement LearningCode0
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?Code0
MDPGT: Momentum-based Decentralized Policy Gradient TrackingCode0
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global StateCode0
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation AssuranceCode0
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement LearningCode0
MAVEN: Multi-Agent Variational ExplorationCode0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
MAC-PO: Multi-Agent Experience Replay via Collective Priority OptimizationCode0
M^3RL: Mind-aware Multi-agent Management Reinforcement LearningCode0
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective IntelligenceCode0
Light Aircraft Game : Basic Implementation and training results analysisCode0
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement LearningCode0
Logic-based Reward Shaping for Multi-Agent Reinforcement LearningCode0
MAHTM: A Multi-Agent Framework for Hierarchical Transactive MicrogridsCode0
Mediated Multi-Agent Reinforcement LearningCode0
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive ReasoningCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel DecodingCode0
Learning to Gather without CommunicationCode0
Learning to Schedule Communication in Multi-agent Reinforcement LearningCode0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement LearningCode0
Learning to Share and Hide Intentions using Information RegularizationCode0
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Benchmark Results

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