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 9626–9650 of 15113 papers

TitleStatusHype
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
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces—0
Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings—0
Deep Reinforcement Learning for Intelligent Transportation Systems—0
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey—0
Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications—0
Deep Reinforcement Learning for Inverse Inorganic Materials Design—0
Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments—0
Deep Reinforcement Learning for Join Order Enumeration—0
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment—0
Deep Reinforcement Learning for Long Term Hydropower Production Scheduling—0
Deep Reinforcement Learning for Long-Term Voltage Stability Control—0
Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality—0
Deep Reinforcement Learning for mmWave Initial Beam Alignment—0
Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes—0
Deep Reinforcement Learning for Motion Planning of Mobile Robots—0
Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling—0
Deep Reinforcement Learning for Multi-objective Optimization—0
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications—0
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem—0
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Benchmark Results

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