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 1611–1620 of 15113 papers

TitleStatusHype
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
Superior Computer Chess with Model Predictive Control, Reinforcement Learning, and Rollout—0
BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design—0
Markov Chain Variance Estimation: A Stochastic Approximation Approach—0
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping—0
Semifactual Explanations for Reinforcement LearningCode0
Forward KL Regularized Preference Optimization for Aligning Diffusion Policies—0
An Introduction to Quantum Reinforcement Learning (QRL)—0
Causality-Driven Reinforcement Learning for Joint Communication and Sensing—0
Reinforcement Learning for Rate Maximization in IRS-aided OWC Networks—0
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Benchmark Results

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