Curriculum Learning for Cumulative Return Maximization

1 code implementation • 13 Jun 2019 • Francesco Foglino, Christiano Coletto Christakou, Ricardo Luna Gutierrez, Matteo Leonetti

We propose a task sequencing algorithm maximizing the cumulative return, that is, the return obtained by the agent across all the learning episodes.

Combinatorial Optimization Transfer Learning

Information-theoretic Task Selection for Meta-Reinforcement Learning

no code implementations • NeurIPS 2020 • Ricardo Luna Gutierrez, Matteo Leonetti

In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to prepare for and learn faster in new, unseen, but related tasks.

Meta Reinforcement Learning reinforcement-learning +1

Meta-Reinforcement Learning for Heuristic Planning

no code implementations • 6 Jul 2021 • Ricardo Luna Gutierrez, Matteo Leonetti

In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to prepare for and learn faster in new, unseen, but related tasks.

Meta Reinforcement Learning reinforcement-learning +1

RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels

no code implementations • 5 Oct 2023 • Alexander Shmakov, Avisek Naug, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna Gutierrez, Ashwin Ramesh Babu, Antonio Guillen, Soumyendu Sarkar

In this paper, we combine recent developments in Deep Kernel Learning (DKL) and attention-based Transformer models to improve the modeling powers of GP surrogates with meta-learning.

Bayesian Optimization Meta-Learning +2

A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration

no code implementations • 18 Apr 2024 • Avisek Naug, Antonio Guillen, Ricardo Luna Gutierrez, Vineet Gundecha, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Soumyendu Sarkar

There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers.