Prakash Panangaden

Core Academic Member

Full Professor, McGill University, School of Computer Science

Biography

Panangaden has been at McGill University since 1990, where for the past twenty-five years he has been working on various aspects of Markov processes: process equivalence, logical characterization, approximation and metrics. Recently he has worked on using metrics to enhance representation learning.

Panangaden first studied physics at the Indian Institute of Technology in Kanpur. For his MSc in physics at the University of Chicago, he studied stimulated emission from black holes, and for his PhD in physics at the University of Wisconsin–Milwaukee, he worked on quantum field theory in curved spacetime.

He was formerly an assistant professor of computer science at Cornell University, where he primarily worked on the semantics of concurrent programming languages.

A Fellow of the Royal Society of Canada and Fellow of the Association for Computing Machinery (ACM), Panangaden has published papers in physics, quantum information and pure mathematics.

Current Students

Jonathan Colaço Carr

Master's Research - McGill University

Co-supervisor :

Doina Precup

jonathan.colaco-carr@mila.quebec

Publications

Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata

Borja Balle

Clara Lacroce

Prakash Panangaden

Doina Precup

Guillaume Rabusseau

We address the approximate minimization problem for weighted finite automata (WFAs) with weights in …

2021-02-13

ArXiv (preprint)

doi.org

arxiv.org

MICo: Improved representations via sampling-based state similarity for Markov decision processes

Pablo Samuel Castro

Tyler Kastner

Prakash Panangaden

Mark Rowland

We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an effe… (see more)ctive means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically difficult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analyses, we provide empirical evidence that learning this distance alongside the value function yields structured and informative representations, including strong results on the Arcade Learning Environment benchmark.

openreview.net

MICo: Learning improved representations via sampling-based state similarity for Markov decision processes

Pablo Samuel Castro

Tyler Kastner

Prakash Panangaden

Mark Rowland

We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an eﬀ… (see more)ective means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically diﬃcult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analysis

2021-01-01

arXiv.org (preprint)

dblp.uni-trier.de

Bisimulation metrics and norms for real-weighted automata

Borja Balle

Pascale Gourdeau

Prakash Panangaden

2020-11-01

Information and Computation (published)

doi.org

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