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Philip Amortila

Alumni

Publications

A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Philip. Amortila
Doina Precup
Prakash Panangaden
Marc Gendron-Bellemare
We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate it… (see more)s effectiveness by presenting simple and unified proofs of convergence for a variety of commonly-used methods. We show that value-based methods such as TD(
2020-03-27
ArXiv (preprint)
arxiv.org
arxiv.org
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Philip Amortila
Doina Precup
Prakash Panangaden
Marc Gendron-Bellemare
2020-01-01
AISTATS (published)
proceedings.mlr.press
arxiv.org
Learning Graph Weighted Models on Pictures
Philip Amortila
Guillaume Rabusseau
Graph Weighted Models (GWMs) have recently been proposed as a natural generalization of weighted automata over strings and trees to arbitrar… (see more)y families of labeled graphs (and hypergraphs). A GWM generically associates a labeled graph with a tensor network and computes a value by successive contractions directed by its edges. In this paper, we consider the problem of learning GWMs defined over the graph family of pictures (or 2-dimensional words). As a proof of concept, we consider regression and classification tasks over the simple Bars & Stripes and Shifting Bits picture languages and provide an experimental study investigating whether these languages can be learned in the form of a GWM from positive and negative examples using gradient-based methods. Our results suggest that this is indeed possible and that investigating the use of gradient-based methods to learn picture series and functions computed by GWMs over other families of graphs could be a fruitful direction.
2018-01-01
ICGI (published)
proceedings.mlr.press
arxiv.org