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 5101–5125 of 15113 papers

TitleStatusHype
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
Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer—0
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management—0
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control—0
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic—0
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
Reinforcement Learning with Lookahead Information—0
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards—0
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach—0
Reinforcement Learning with Neural Radiance Fields—0
Reinforcement Learning with Non-Exponential Discounting—0
Reinforcement Learning with Non-uniform State Representations for Adaptive Search—0
Reinforcement Learning without Ground-Truth State—0
Reinforcement Learning with Partial Parametric Model Knowledge—0
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Benchmark Results

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