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 1376–1400 of 1718 papers

TitleStatusHype
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?Code1
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—0
Scalable Reinforcement Learning Policies for Multi-Agent ControlCode1
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural NetworkCode1
Emergent Reciprocity and Team Formation from Randomized Uncertain Social PreferencesCode2
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
Learning a Decentralized Multi-arm Motion PlannerCode1
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence—0
Multi-Agent Reinforcement Learning for Visibility-based Persistent MonitoringCode0
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning—0
Game-Theoretic Multiagent Reinforcement LearningCode1
Online Learning in Unknown Markov Games—0
Succinct and Robust Multi-Agent Communication With Temporal Message ControlCode1
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement LearningCode1
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—0
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous DrivingCode2
Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading—0
Multi-Agent Collaboration via Reward Attribution DecompositionCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
Multi-Agent Trust Region Policy OptimizationCode0
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards—0
Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion—0
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified