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 1676–1700 of 1718 papers

TitleStatusHype
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning—0
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning—0
Coordination-driven learning in multi-agent problem spaces—0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
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
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue ModellingCode0
Entropy based Independent Learning in Anonymous Multi-Agent Settings—0
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings—0
Intent-aware Multi-agent Reinforcement Learning—0
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising—0
Modeling Others using Oneself in Multi-Agent Reinforcement Learning—0
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning—0
Learning to Gather without CommunicationCode0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication—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