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 4031–4040 of 15113 papers

TitleStatusHype
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
A comparative evaluation of machine learning methods for robot navigation through human crowds—0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
A Comparative Study of Deep Reinforcement Learning for Crop Production Management—0
A Comparative Study of Reinforcement Learning Techniques on Dialogue Management—0
A Comparison of Action Spaces for Learning Manipulation Tasks—0
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control—0
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies—0
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Benchmark Results

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