SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 3091–3100 of 15113 papers

TitleStatusHype
Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning—0
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective—0
A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
A Survey of Multi-Agent Deep Reinforcement Learning with Communication—0
A review of motion planning algorithms for intelligent robotics—0
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC—0
A Survey of Meta-Reinforcement Learning—0
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound—0
AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified