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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 81–90 of 15113 papers

TitleStatusHype
IntelliLung: Advancing Safe Mechanical Ventilation using Offline RL with Hybrid Actions and Clinically Aligned Rewards—0
Reasoning with Exploration: An Entropy Perspective—0
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs—0
Unsupervised Skill Discovery through Skill Regions Differentiation—0
HiLight: A Hierarchical Reinforcement Learning Framework with Global Adversarial Guidance for Large-Scale Traffic Signal Control—0
Zeroth-Order Optimization is Secretly Single-Step Policy Optimization—0
Adaptive Reinforcement Learning for Unobservable Random Delays—0
Metis-RISE: RL Incentivizes and SFT Enhances Multimodal Reasoning Model LearningCode1
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning—0
Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning—0
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Benchmark Results

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