50 citations · 275 across the 56 of their papers we have counts for
9 papers · 1 filter
Transfer RL via the Undo Maps Formalism
Abhi Gupta, Ted Moskovitz, David Alvarez-Melis +1
Transferring knowledge across domains is one of the most fundamental problems in machine learning, but doing so effectively in the context of reinforcement learning remains largely…
Leveraging Offline Data in Online Reinforcement Learning
Andrew Wagenmaker, Aldo Pacchiano
Two central paradigms have emerged in the reinforcement learning (RL) community: online RL and offline RL. In the online RL setting, the agent has no prior knowledge of the environ…
Learning General World Models in a Handful of Reward-Free Deployments
Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano +5
Building generally capable agents is a grand challenge for deep reinforcement learning (RL). To approach this challenge practically, we outline two key desiderata: 1) to facilitate…
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai +2
Reinforcement learning provides an automated framework for learning behaviors from high-level reward specifications, but in practice the choice of reward function can be crucial fo…
Neural Design for Genetic Perturbation Experiments
Aldo Pacchiano, Drausin Wulsin, Robert A. Barton +1
The problem of how to genetically modify cells in order to maximize a certain cellular phenotype has taken center stage in drug development over the last few years (with, for examp…
Best of Both Worlds Model Selection
Aldo Pacchiano, Christoph Dann, Claudio Gentile
We study the problem of model selection in bandit scenarios in the presence of nested policy classes, with the goal of obtaining simultaneous adversarial and stochastic ("best of b…
Joint Representation Training in Sequential Tasks with Shared Structure
Aldo Pacchiano, Ofir Nachum, Nilseh Tripuraneni +1
Classical theory in reinforcement learning (RL) predominantly focuses on the single task setting, where an agent learns to solve a task through trial-and-error experience, given ac…
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
Tianyi Lin, Aldo Pacchiano, Yaodong Yu +1
Motivated by applications to online learning in sparse estimation and Bayesian optimization, we consider the problem of online unconstrained nonsubmodular minimization with delayed…
Meta Learning MDPs with Linear Transition Models
Robert Müller, Aldo Pacchiano
We study meta-learning in Markov Decision Processes (MDP) with linear transition models in the undiscounted episodic setting. Under a task sharedness metric based on model proximit…