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 811–820 of 15113 papers

TitleStatusHype
Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement LearningCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Behavior Proximal Policy OptimizationCode1
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
Swapped goal-conditioned offline reinforcement learningCode1
Dual RL: Unification and New Methods for Reinforcement and Imitation LearningCode1
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement LearningCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
Procedural generation of meta-reinforcement learning tasksCode1
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Benchmark Results

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