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 1301–1310 of 15113 papers

TitleStatusHype
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Evaluating Long-Term Memory in 3D MazesCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement LearningCode1
Entropy-Regularized Process Reward ModelCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
A Crash Course on Reinforcement LearningCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
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Benchmark Results

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