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

Showing 876900 of 1718 papers

TitleStatusHype
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement LearningCode0
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning0
MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems0
Non-Linear Coordination Graphs0
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning0
Teal: Learning-Accelerated Optimization of WAN Traffic EngineeringCode1
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games0
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement LearningCode0
Solving Continuous Control via Q-learningCode1
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation0
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning0
Proximal Learning With Opponent-Learning AwarenessCode0
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning0
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning0
Multi-Agent Automated Machine Learning0
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement LearningCode0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning0
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations0
Phantom -- A RL-driven multi-agent framework to model complex systemsCode1
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement LearningCode0
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian LearningCode0
Multiagent Reinforcement Learning Based on Fusion-Multiactor-Attention-Critic for Multiple-Unmanned-Aerial-Vehicle Navigation ControlCode1
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic RewardCode1
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Benchmark Results

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