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 2941–2950 of 15113 papers

TitleStatusHype
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method—0
Controlling Neural Style Transfer with Deep Reinforcement Learning—0
Improving Planning with Large Language Models: A Modular Agentic ArchitectureCode1
Learning to Rewrite Prompts for Personalized Text Generation—0
Reinforcement Learning for Node Selection in Branch-and-Bound—0
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and SmoothnessCode0
AdaRefiner: Refining Decisions of Language Models with Adaptive FeedbackCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning—0
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Benchmark Results

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