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 1151–1160 of 15113 papers

TitleStatusHype
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models—0
Harvesting energy from turbulent winds with Reinforcement Learning—0
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous InferenceCode1
CLIP-RLDrive: Human-Aligned Autonomous Driving via CLIP-Based Reward Shaping in Reinforcement Learning—0
Guiding Generative Protein Language Models with Reinforcement LearningCode2
Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement LearningCode0
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
Multi-Task Reinforcement Learning for Quadrotors—0
Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions—0
ParMod: A Parallel and Modular Framework for Learning Non-Markovian Tasks—0
Show:102550
← PrevPage 116 of 1512Next →

Benchmark Results

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