6 papers · 1 filter
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…
On the Hardness of Bandit Learning
Nataly Brukhim, Aldo Pacchiano, Miroslav Dudik +1
We study the task of bandit learning, also known as best-arm identification, under the assumption that the true reward function f belongs to a known, but arbitrary, function class…
Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms
Xinyi Hu, Aldo Pacchiano
We study the decentralized multi-player multi-armed bandits (MMAB) problem under a no-sensing setting, where each player receives only their own reward and obtains no information a…
In-Context Learning for Pure Exploration
Alessio Russo, Ryan Welch, Aldo Pacchiano
We study the problem active sequential hypothesis testing, also known as pure exploration: given a new task, the learner adaptively collects data from the environment to efficientl…
Pure Exploration with Feedback Graphs
Alessio Russo, Yichen Song, Aldo Pacchiano
We study the sample complexity of pure exploration in an online learning problem with a feedback graph. This graph dictates the feedback available to the learner, covering scenario…
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation
Alessio Russo, Aldo Pacchiano
We study the policy evaluation problem in an online multi-reward multi-policy discounted setting, where multiple reward functions must be evaluated simultaneously for different pol…