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 9351–9375 of 15113 papers

TitleStatusHype
Reinforcement Learning with Differential Privacy—0
Reinforcement learning with distance-based incentive/penalty (DIP) updates for highly constrained industrial control systems—0
Reinforcement Learning with Efficient Active Feature Acquisition—0
Reinforcement Learning with Elastic Time Steps—0
Reinforcement learning with experience replay and adaptation of action dispersion—0
Reinforcement Learning with Expert Trajectory For Quantitative Trading—0
Reinforcement Learning with Ex-Post Max-Min Fairness—0
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads—0
Reinforcement Learning with External Knowledge by using Logical Neural Networks—0
Reinforcement Learning with Feedback-modulated TD-STDP—0
Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills—0
Reinforcement Learning with Feedback Graphs—0
Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts—0
Reinforcement Learning with Function Approximation: From Linear to Nonlinear—0
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control—0
Reinforcement Learning with Generalizable Gaussian Splatting—0
Reinforcement Learning with General LTL Objectives is Intractable—0
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space—0
Reinforcement Learning with Goal-Distance Gradient—0
Reinforcement Learning with Graph Attention for Routing and Wavelength Assignment with Lightpath Reuse—0
Reinforcement Learning with Heterogeneous Data: Estimation and Inference—0
Reinforcement Learning with History-Dependent Dynamic Contexts—0
Reinforcement learning with human advice: a survey—0
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation—0
Reinforcement Learning with Information-Theoretic Actuation—0
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Benchmark Results

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