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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

Showing 6170 of 1718 papers

TitleStatusHype
SRMT: Shared Memory for Multi-agent Lifelong PathfindingCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMACCode1
Multi Agent Reinforcement Learning for Sequential Satellite Assignment ProblemsCode1
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against RadarCode1
HyperMARL: Adaptive Hypernetworks for Multi-Agent RLCode1
Learning to Cooperate with Humans using Generative AgentsCode1
InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemmaCode1
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement LearningCode1
PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal ControlCode1
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Benchmark Results

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