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 1851–1860 of 15113 papers

TitleStatusHype
A Max-Min Entropy Framework for Reinforcement LearningCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction GuaranteesCode1
Reinforcement Learning Framework for Deep Brain Stimulation StudyCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Reinforcement Learning on Web Interfaces Using Workflow-Guided ExplorationCode1
Reinforcement Learning Policy as Macro Regulator Rather than Macro PlacerCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
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Benchmark Results

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