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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 1381–1390 of 1718 papers

TitleStatusHype
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
Shaping Advice in Deep Multi-Agent Reinforcement LearningCode0
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning—0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target PredictionCode0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Learning to Robustly Negotiate Bi-Directional Lane Usage in High-Conflict Driving Scenarios—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—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