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 1291–1300 of 1718 papers

TitleStatusHype
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—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
Shaping Advice in Deep Multi-Agent Reinforcement LearningCode0
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
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
Learning to Robustly Negotiate Bi-Directional Lane Usage in High-Conflict Driving Scenarios—0
Regularized Softmax Deep Multi-Agent Q-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