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 1126–1150 of 1718 papers

TitleStatusHype
Collaboration Promotes Group Resilience in Multi-Agent AI—0
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic—0
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement LearningCode0
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power SystemsCode1
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent ProblemsCode1
Multi Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks—0
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—0
A Supervised-Learning based Hour-Ahead Demand Response of a Behavior-based HEMS approximating MILP Optimization—0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System—0
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning—0
Crowd-sensing Enhanced Parking Patrol using Trajectories of Sharing Bikes—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—0
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution NetworksCode1
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Multi-Agent Advisor Q-LearningCode0
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey—0
Learning to Simulate Self-Driven Particles System with Coordinated Policy OptimizationCode1
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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