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 1961–1970 of 15113 papers

TitleStatusHype
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware DetectionCode1
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement LearningCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Sequential Voting with Relational Box Fields for Active Object DetectionCode1
A Multiplicative Value Function for Safe and Efficient Reinforcement LearningCode1
SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited DataCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical PerspectivesCode1
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
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Benchmark Results

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