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 1151–1175 of 1718 papers

TitleStatusHype
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines—0
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations—0
Towards Resilience for Multi-Agent QD-Learning—0
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization—0
Tractable Equilibrium Computation in Markov Games through Risk Aversion—0
Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning—0
Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Study—0
Transferable and Distributed User Association Policies for 5G and Beyond Networks—0
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents—0
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication—0
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems—0
Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow—0
Turn-based Multi-Agent Reinforcement Learning Model Checking—0
Two-stage training algorithm for AI robot soccer—0
Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes—0
Ultra-dense Low Data Rate (UDLD) Communication in the THz—0
Truthful Self-Play—0
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning—0
Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning—0
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent 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