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 4001–4010 of 15113 papers

TitleStatusHype
Assessing Policy, Loss and Planning Combinations in Reinforcement Learning using a New Modular Architecture—0
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
Development of collective behavior in newborn artificial agents—0
Bayesian Linear Regression on Deep Representations—0
Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence—0
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation—0
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning—0
A Generalized Natural Actor-Critic Algorithm—0
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Benchmark Results

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