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 1741–1750 of 15113 papers

TitleStatusHype
NAVIX: Scaling MiniGrid Environments with JAXCode2
Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers—0
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning—0
Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning—0
Pretrained Visual Representations in Reinforcement Learning—0
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems—0
Gymnasium: A Standard Interface for Reinforcement Learning EnvironmentsCode11
Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach—0
SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning—0
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
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Benchmark Results

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