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 1671–1680 of 1718 papers

TitleStatusHype
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising—0
MARL-FWC: Optimal Coordination of Freeway Traffic Control Measures—0
Learning to Share and Hide Intentions using Information RegularizationCode0
Multi-Agent Reinforcement Learning: A Report on Challenges and ApproachesCode0
Learning to Act in Decentralized Partially Observable MDPs—0
Learning Existing Social Conventions via Observationally Augmented Self-Play—0
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization—0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
On Gradient-Based Learning in Continuous Games—0
Emergent Communication through Negotiation—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