4 papers
Bayesian Online Model Selection
Aida Afshar, Yuke Zhang, Aldo Pacchiano
Online model selection in Bayesian bandits raises a fundamental exploration challenge: When an environment instance is sampled from a prior distribution, how can we design an adapt…
Improved Training Mechanism for Reinforcement Learning via Online Model Selection
Aida Afshar, Aldo Pacchiano
We study the problem of online model selection in reinforcement learning, where the selector has access to a class of reinforcement learning agents and learns to adaptively select…
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives
Aida Afshar, Aldo Pacchiano
The performance of reinforcement learning (RL) algorithms is sensitive to the choice of hyperparameters, with the learning rate being particularly influential. RL algorithms fail t…
DeLF: Designing Learning Environments with Foundation Models
Aida Afshar, Wenchao Li
Reinforcement learning (RL) offers a capable and intuitive structure for the fundamental sequential decision-making problem. Despite impressive breakthroughs, it can still be diffi…