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 2711–2720 of 15113 papers

TitleStatusHype
Automatic Document Sketching: Generating Drafts from Analogous Texts—0
Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning—0
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Aligning Language Models with Offline Learning from Human Feedback—0
Adaptive Graph Capsule Convolutional Networks—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning—0
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning—0
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback—0
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Benchmark Results

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