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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

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Showing 1176–1200 of 1718 papers

TitleStatusHype
Truthful Self-Play—0
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution—0
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control—0
Universal Agent Mixtures and the Geometry of Intelligence—0
Multi-agent Attacks for Black-box Social Recommendations—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
Using a single actor to output personalized policy for different intersections—0
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach—0
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training—0
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning—0
Value Variance Minimization for Learning Approximate Equilibrium in Aggregation Systems—0
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings—0
Variational Inequality Methods for Multi-Agent Reinforcement Learning: Performance and Stability Gains—0
Variational Offline Multi-agent Skill Discovery—0
VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework—0
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning—0
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning—0
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning—0
Visual Theory of Mind Enables the Invention of Proto-Writing—0
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT—0
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Benchmark Results

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