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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1026–1050 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning—0
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement LearningCode0
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning—0
Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation—0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Decentralized Policy Optimization—0
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 Learning—0
MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems—0
Non-Linear Coordination Graphs—0
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning—0
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement LearningCode0
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games—0
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation—0
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning—0
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning—0
Proximal Learning With Opponent-Learning AwarenessCode0
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning—0
Multi-Agent Automated Machine Learning—0
Show:102550
← PrevPage 42 of 69Next →
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