SOTAVerified

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

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Showing 26–50 of 1718 papers

TitleStatusHype
CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients—0
Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods—0
LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation—0
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation—0
Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning—0
RLAE: Reinforcement Learning-Assisted Ensemble for LLMs—0
Sorrel: A simple and flexible framework for multi-agent reinforcement learningCode1
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement LearningCode0
Information Structure in Mappings: An Approach to Learning, Representation, and Generalisation—0
Reward-Independent Messaging for Decentralized Multi-Agent Reinforcement Learning—0
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired PerspectiveCode0
The challenge of hidden gifts in multi-agent reinforcement learning—0
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications—0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks—0
Agent-Based Decentralized Energy Management of EV Charging Station with Solar Photovoltaics via Multi-Agent Reinforcement Learning—0
Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning—0
Smart Traffic Signals: Comparing MARL and Fixed-Time StrategiesCode0
Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks—0
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified