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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 15211530 of 1718 papers

TitleStatusHype
Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative DrivingCode0
Multi-Agent Advisor Q-LearningCode0
OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous VehiclesCode0
Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and qualityCode0
MAVEN: Multi-Agent Variational ExplorationCode0
Multi-Agent Common Knowledge Reinforcement LearningCode0
The Composite Task Challenge for Cooperative Multi-Agent Reinforcement LearningCode0
Multi-Agent Congestion Cost Minimization With Linear Function ApproximationsCode0
MAHTM: A Multi-Agent Framework for Hierarchical Transactive MicrogridsCode0
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic ScenarioCode0
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Benchmark Results

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