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 2771–2780 of 15113 papers

TitleStatusHype
Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning—0
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning—0
Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy—0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—0
Handling Delay in Real-Time Reinforcement LearningCode0
Reinforcement Learning-based Token Pruning in Vision Transformers: A Markov Game ApproachCode0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection—0
Reinforcement Learning for Active Matter—0
RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations—0
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL—0
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Benchmark Results

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