Fabio Pardo

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Imperial College London

I am a fourth-year PhD student in the Robot Intelligence Lab at Imperial College London.

My main research focuses on Deep Reinforcement Learning.

https://fabiopardo.github.io/

Featured Co-authors

Stephen James
25 publications
Ronald Clark
22 publications
Petar Kormushev
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Yuval Tassa
20 publications
Arash Tavakoli
13 publications
Fabian Falck
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Daniel Lenton
4 publications
Vitaly Levdik
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Vittorio La Barbera
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Monica Daley
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Christopher Richards
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∙ 12/11/2021

OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion

Muscle-actuated control is a research topic of interest spanning differe...

Vittorio La Barbera, et al. ∙

∙ 02/04/2021

Ivy: Templated Deep Learning for Inter-Framework Portability

We introduce Ivy, a templated Deep Learning (DL) framework which abstrac...

Daniel Lenton, et al. ∙

∙ 11/24/2020

Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks

Being able to reach any desired location in the environment can be a val...

Fabio Pardo, et al. ∙

∙ 11/24/2020

CoMic: Complementary Task Learning & Mimicry for Reusable Skills

Learning to control complex bodies and reuse learned behaviors is a long...

Fabio Pardo, et al. ∙

∙ 11/24/2020

Time Limits in Reinforcement Learning

In reinforcement learning, it is common to let an agent interact for a f...

Fabio Pardo, et al. ∙

∙ 11/15/2020

Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking

Deep reinforcement learning has been one of the fastest growing fields o...

Fabio Pardo, et al. ∙

∙ 10/06/2018

Q-map: a Convolutional Approach for Goal-Oriented Reinforcement Learning

Goal-oriented learning has become a core concept in reinforcement learni...

Fabio Pardo, et al. ∙

∙ 07/05/2018

Goal-oriented Trajectories for Efficient Exploration

Exploration is a difficult challenge in reinforcement learning and even ...

Fabio Pardo, et al. ∙

∙ 11/24/2017

Action Branching Architectures for Deep Reinforcement Learning

Discrete-action algorithms have been central to numerous recent successe...

Arash Tavakoli, et al. ∙