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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 1251–1275 of 1718 papers

TitleStatusHype
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Crowd-sensing Enhanced Parking Patrol using Trajectories of Sharing Bikes—0
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System—0
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—0
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey—0
Multi-Agent Advisor Q-LearningCode0
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning—0
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming—0
Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level—0
Statistical discrimination in learning agents—0
Independent Natural Policy Gradient Always Converges in Markov Potential Games—0
State-based Episodic Memory for Multi-Agent Reinforcement Learning—0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks—0
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning—0
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge—0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—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