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 3681–3690 of 15113 papers

TitleStatusHype
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
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
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
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems—0
Pretrained Visual Representations in Reinforcement Learning—0
Automatic Environment Shaping is the Next Frontier in RL—0
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Benchmark Results

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