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 121–130 of 15113 papers

TitleStatusHype
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications—0
Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy—0
Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows—0
ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMsCode1
Optimal Operating Strategy for PV-BESS Households: Balancing Self-Consumption and Self-Sufficiency—0
TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy OptimizationCode0
Policy-Based Trajectory Clustering in Offline Reinforcement Learning—0
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement LearningCode2
SPEED-RL: Faster Training of Reasoning Models via Online Curriculum LearningCode1
DeepForm: Reasoning Large Language Model for Communication System Formulation—0
Show:102550
← PrevPage 13 of 1512Next →

Benchmark Results

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