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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 1076–1100 of 1718 papers

TitleStatusHype
Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents—0
Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies—0
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning—0
Skill Discovery of Coordination in Multi-agent Reinforcement Learning—0
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters—0
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning—0
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition—0
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning—0
SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control—0
Social Motion Prediction with Cognitive Hierarchies—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning—0
Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL—0
Solving Stochastic Games—0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Sparse Mean Field Load Balancing in Large Localized Queueing Systems—0
Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios—0
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
SSoC: Learning Spontaneous and Self-Organizing Communication for Multi-Agent Collaboration—0
Stabilizing Q Learning Via Soft Mellowmax Operator—0
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems—0
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula—0
State-based Episodic Memory for Multi-Agent Reinforcement Learning—0
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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