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Found 6 papers, 4 papers with code
Learning Optimal Deterministic Policies with Stochastic Policy Gradients
After introducing a novel framework for modeling this scenario, we study the global convergence to the best deterministic policy, under (weak) gradient domination assumptions.
Best Arm Identification for Stochastic Rising Bandits
Then, we prove that, with a sufficiently large budget, they provide guarantees on the probability of properly identifying the optimal option at the end of the learning process.
Autoregressive Bandits
Autoregressive processes naturally arise in a large variety of real-world scenarios, including stock markets, sales forecasting, weather prediction, advertising, and pricing.
Dynamic Pricing with Volume Discounts in Online Settings
We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B-corresponding to A configuration-has been compared with human pricing specialists-corresponding to B configuration.
Dynamical Linear Bandits
Then, the hidden state evolves according to linear dynamics, affected by the performed action too.
ARLO: A Framework for Automated Reinforcement Learning
In this work, we propose a general and flexible framework, namely ARLO: Automated Reinforcement Learning Optimizer, to construct automated pipelines for AutoRL.
