12 citations · 42 across the 7 of their papers we have counts for
7 papers
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion
Vittorio La Barbera, Fabio Pardo, Yuval Tassa +4
Muscle-actuated control is a research topic that spans multiple domains, including biomechanics, neuroscience, reinforcement learning, robotics, and graphics. This type of control…
Ivy: Templated Deep Learning for Inter-Framework Portability
Daniel Lenton, Fabio Pardo, Fabian Falck +2
We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks. Ivy unifies the core functions of these frameworks to exhibit consistent call sig…
Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking
Fabio Pardo
Deep reinforcement learning has been one of the fastest growing fields of machine learning over the past years and numerous libraries have been open sourced to support research. Ho…
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Fabio Pardo, Vitaly Levdik, Petar Kormushev
Being able to reach any desired location in the environment can be a valuable asset for an agent. Learning a policy to navigate between all pairs of states individually is often no…
Goal-oriented Trajectories for Efficient Exploration
Fabio Pardo, Vitaly Levdik, Petar Kormushev
Exploration is a difficult challenge in reinforcement learning and even recent state-of-the art curiosity-based methods rely on the simple epsilon-greedy strategy to generate novel…
Time Limits in Reinforcement Learning
Fabio Pardo, Arash Tavakoli, Vitaly Levdik +1
In reinforcement learning, it is common to let an agent interact for a fixed amount of time with its environment before resetting it and repeating the process in a series of episod…
Action Branching Architectures for Deep Reinforcement Learning
Arash Tavakoli, Fabio Pardo, Petar Kormushev
Discrete-action algorithms have been central to numerous recent successes of deep reinforcement learning. However, applying these algorithms to high-dimensional action tasks requir…