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 3921–3930 of 15113 papers

TitleStatusHype
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction—0
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing—0
Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation—0
Demonstration-Regularized RL—0
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation—0
Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning—0
Assessing Transferability from Simulation to Reality for Reinforcement Learning—0
Demystify Painting with RL—0
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning—0
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Benchmark Results

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