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 921–930 of 15113 papers

TitleStatusHype
Zero-Shot Reinforcement Learning from Low Quality DataCode1
Analysis of diversity-accuracy tradeoff in image captioningCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Giraffe: Using Deep Reinforcement Learning to Play ChessCode1
A Max-Min Entropy Framework for Reinforcement LearningCode1
GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical ReasoningCode1
A Multiplicative Value Function for Safe and Efficient Reinforcement LearningCode1
Conservative Offline Distributional Reinforcement LearningCode1
Goal-Conditioned Reinforcement Learning: Problems and SolutionsCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
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Benchmark Results

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