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 501–510 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC SystemsCode1
Contrastive Active InferenceCode1
Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular NetworksCode1
A Composable Specification Language for Reinforcement Learning TasksCode1
Deep Reinforcement Learning for List-wise RecommendationsCode1
A Comprehensive Survey of Data Augmentation in Visual Reinforcement LearningCode1
Adaptive Transformers in RLCode1
Deep Reinforcement Learning for Producing Furniture Layout in Indoor ScenesCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
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Benchmark Results

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