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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 826–850 of 1718 papers

TitleStatusHype
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles—0
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control—0
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation—0
MARLIM: Multi-Agent Reinforcement Learning for Inventory Management—0
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning—0
Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach—0
Improving International Climate Policy via Mutually Conditional Binding Commitments—0
Consensus-based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning—0
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication—0
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems—0
Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes—0
QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent Reinforcement Learning—0
VISER: A Tractable Solution Concept for Games with Information AsymmetryCode0
Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning—0
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints—0
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning—0
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning—0
Emergent Resource Exchange and Tolerated Theft Behavior using Multi-Agent Reinforcement Learning—0
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning—0
Enhancing the Robustness of QMIX against State-adversarial Attacks—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
Discovering Causality for Efficient Cooperation in Multi-Agent EnvironmentsCode0
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction—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