1 code implementation • 15 Feb 2024 • Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov, Aladin Virmaux, Giuseppe Paolo, Themis Palpanas, Ievgen Redko
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting.
no code implementations • 6 Feb 2024 • Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models.
no code implementations • 5 Feb 2024 • Abdelhakim Benechehab, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Balázs Kégl
In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data.
no code implementations • 9 Oct 2023 • Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 20 Jun 2022 • Giuseppe Paolo, Jonas Gonzalez-Billandon, Albert Thomas, Balázs Kégl
In the last decade, reinforcement learning successfully solved complex control tasks and decision-making problems, like the Go board game.
no code implementations • 2 Mar 2022 • Giuseppe Paolo
In this thesis, we approach the problem of sparse rewards with these algorithms, and in particular with Novelty Search (NS).
1 code implementation • 2 Nov 2021 • Giuseppe Paolo, Miranda Coninx, Alban Laflaquière, Stephane Doncieux
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions.
1 code implementation • 5 Feb 2021 • Giuseppe Paolo, Alexandre Coninx, Stephane Doncieux, Alban Laflaquière
Contrary to existing emitters-based approaches, SERENE separates the search space exploration and reward exploitation into two alternating processes.
2 code implementations • 13 May 2020 • Stephane Doncieux, Giuseppe Paolo, Alban Laflaquière, Alexandre Coninx
Evolvability is thus a natural byproduct of the search in this context.
1 code implementation • 12 Sep 2019 • Giuseppe Paolo, Alban Laflaquière, Alexandre Coninx, Stephane Doncieux
Results show that TAXONS can find a diverse set of controllers, covering a good part of the ground-truth outcome space, while having no information about such space.
no code implementations • 25 Sep 2017 • Giuseppe Paolo, Lei Tai, Ming Liu
In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach.
no code implementations • 25 Sep 2017 • Mark Pfeiffer, Giuseppe Paolo, Hannes Sommer, Juan Nieto, Roland Siegwart, Cesar Cadena
This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles.
Robotics
2 code implementations • 1 Mar 2017 • Lei Tai, Giuseppe Paolo, Ming Liu
We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output.