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 2650 of 1718 papers

TitleStatusHype
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous DrivingCode2
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement LearningCode2
Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource AllocationCode2
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot LearningCode2
ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement LearningCode2
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement LearningCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object TrackingCode2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
Emergent Reciprocity and Team Formation from Randomized Uncertain Social PreferencesCode2
Ensembling Prioritized Hybrid Policies for Multi-agent PathfindingCode2
Heterogeneous Multi-Robot Reinforcement LearningCode2
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning BenchmarksCode2
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement LearningCode2
MAexp: A Generic Platform for RL-based Multi-Agent ExplorationCode2
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language ModelsCode2
Maximum Entropy Heterogeneous-Agent Reinforcement LearningCode2
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-DependencyCode2
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online AdvertisingCode1
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal ControlCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
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Benchmark Results

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