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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 201–225 of 1718 papers

TitleStatusHype
Generative Emergent Communication: Large Language Model is a Collective World Model—0
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach—0
Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics—0
Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning—0
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding—0
SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMACCode1
Multi Agent Reinforcement Learning for Sequential Satellite Assignment ProblemsCode1
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning—0
AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning—0
The impact of behavioral diversity in multi-agent reinforcement learning—0
Investigating Relational State Abstraction in Collaborative MARLCode0
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning—0
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs—0
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning—0
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against RadarCode1
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing—0
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks—0
GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic—0
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning—0
Steganography in Game Actions—0
Learn How to Query from Unlabeled Data Streams in Federated LearningCode0
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization—0
Augmenting the action space with conventions to improve multi-agent cooperation in HanabiCode0
HyperMARL: Adaptive Hypernetworks for Multi-Agent RLCode1
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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