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 3631–3640 of 15113 papers

TitleStatusHype
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation—0
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing—0
Deep Reinforcement Learning for Green Security Games with Real-Time Information—0
Deep reinforcement learning for guidewire navigation in coronary artery phantom—0
Deep Reinforcement Learning for Haptic Shared Control in Unknown Tasks—0
Deep Reinforcement Learning for Heat Pump Control—0
Deep Reinforcement Learning for High Precision Assembly Tasks—0
Deep Reinforcement Learning for High Level Character Control—0
Deep Reinforcement Learning for Image Hashing—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
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Benchmark Results

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