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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 741–750 of 15113 papers

TitleStatusHype
A General Contextualized Rewriting Framework for Text SummarizationCode1
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply ChainsCode1
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and SimplicityCode1
Deep Reinforcement Learning for Entity AlignmentCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Deep Reinforcement Learning for List-wise RecommendationsCode1
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning StudyCode1
Deep Reinforcement Learning for Process SynthesisCode1
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environmentsCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
Show:102550
← PrevPage 75 of 1512Next →

Benchmark Results

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