Pablo Hernandez Leal

Photo of Pablo Hernandez Leal

Researcher

PhD Computer Science, Instituto Nacional de Astrofísica, Optica y Electronica

Pablo is a researcher working in the group led by Matt Taylor. Pablo is interested in how learning algorithms developed for single-agent environments should be adapted to multiagent settings. One of his objectives is to propose efficient multiagent learning algorithms for strategic interactions using models and concepts from game theory, Bayesian reasoning, and reinforcement learning. 

Before joining Borealis Pablo studied at INAOE in Mexico and at Washington State University in the USA, later he worked as a researcher at CWI, the National Research Institute for Mathematics and Computer Science of the Netherlands.

Born and raised in Mexico, Pablo loves tacos and spicy food. After living in Amsterdam for a couple of years, he likes biking to work although the Canadian weather sometimes makes this impossible.

pablo.hernandez@borealisai.com

Research Areas

Reinforcement Learning

Publications

May 13, 2019

Safer Deep RL with Shallow MCTS: A Case Study in Pommerman

Workshop on Adaptive Learning Agents (AAMAS), 2019
Authors: B. Kartal , P. Hernandez-Leal , C. Gao, M. Taylor
May 15, 2019

Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition

The Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019
Authors: C. Gao, P. Hernandez-Leal , B. Kartal , M. Taylor
Picture of Pablo Hernandez Leal