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 1341–1350 of 15113 papers

TitleStatusHype
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
A Crash Course on Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
Evolving Reinforcement Learning AlgorithmsCode1
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Benchmark Results

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