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 3221–3230 of 15113 papers

TitleStatusHype
Data-Driven Evaluation of Training Action Space for Reinforcement Learning—0
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
A Survey of Continual Reinforcement Learning—0
Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games—0
Data-Driven Learning and Load Ensemble Control—0
Data-Driven LQR using Reinforcement Learning and Quadratic Neural Networks—0
Data-driven modeling and supervisory control system optimization for plug-in hybrid electric vehicles—0
Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey—0
Data-Driven Offline Decision-Making via Invariant Representation Learning—0
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps—0
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Benchmark Results

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