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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 751–800 of 1718 papers

TitleStatusHype
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning—0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
Improving International Climate Policy via Mutually Conditional Binding Commitments—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Group-Agent Reinforcement Learning—0
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios—0
Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space—0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks—0
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration—0
Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learning—0
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—0
Impression Allocation and Policy Search in Display Advertising—0
Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach—0
Implementations that Matter in Cooperative Multi-Agent Reinforcement Learning—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem—0
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—0
AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL—0
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data—0
HypRL: Reinforcement Learning of Control Policies for Hyperproperties—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing—0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning—0
Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization—0
Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning—0
Hypernetwork-based approach for optimal composition design in partially controlled multi-agent systems—0
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
CURO: Curriculum Learning for Relative Overgeneralization—0
Kindness in Multi-Agent Reinforcement Learning—0
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles—0
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate—0
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning—0
Human-Machine Dialogue as a Stochastic Game—0
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation—0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System—0
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—0
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
A MARL-based Approach for Easing MAS Organization Engineering—0
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning—0
How much can change in a year? Revisiting Evaluation in 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