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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

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Showing 511–520 of 1718 papers

TitleStatusHype
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—0
Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamicsCode0
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense—0
Multi-agent Reinforcement Learning: A Comprehensive Survey—0
Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning—0
Adaptive parameter sharing for multi-agent reinforcement learning—0
Efficiently Quantifying Individual Agent Importance in Cooperative MARL—0
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning—0
On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—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