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 1371–1380 of 1718 papers

TitleStatusHype
Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RL—0
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing—0
Two-stage training algorithm for AI robot soccer—0
Towards Resilience for Multi-Agent QD-Learning—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges—0
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning—0
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World—0
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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