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 1351–1400 of 1718 papers

TitleStatusHype
Communication in Multi-Agent Reinforcement Learning: Intention Sharing—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning—0
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient OptimizationCode1
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Cooperative Policy Learning with Pre-trained Heterogeneous Observation RepresentationsCode1
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningCode0
Difference Rewards Policy Gradients—0
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning—0
Stabilizing Q Learning Via Soft Mellowmax Operator—0
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement LearningCode1
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Learning Multi-Agent Communication through Structured Attentive ReasoningCode1
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems—0
Minimax Sample Complexity for Turn-based Stochastic Game—0
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement LearningCode1
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control—0
Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment—0
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