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 2981–2990 of 15113 papers

TitleStatusHype
Text2Reward: Reward Shaping with Language Models for Reinforcement LearningCode2
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces—0
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence—0
Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving—0
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration—0
Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules—0
Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments—0
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning—0
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Benchmark Results

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