no code implementations • 8 Feb 2024 • Giorgio Angelotti, Caroline P. C. Chanel, Adam H. M. Pinto, Christophe Lounis, Corentin Chauffaut, Nicolas Drougard
The integration of physiological computing into mixed-initiative human-robot interaction systems offers valuable advantages in autonomous task allocation by incorporating real-time features as human state observations into the decision-making system.
no code implementations • 30 Oct 2023 • Giorgio Angelotti
In Transformer-based architectures, the attention mechanism is inherently permutation-invariant with respect to the input sequence's tokens.
1 code implementation • 6 Dec 2022 • Giorgio Angelotti, Natalia Díaz-Rodríguez
A quantitative assessment of the global importance of an agent in a team is as valuable as gold for strategists, decision-makers, and sports coaches.
1 code implementation • 18 Dec 2021 • Giorgio Angelotti, Nicolas Drougard, Caroline P. C. Chanel
Offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task that greatly depends on the data available in the learning phase.
no code implementations • 19 Nov 2021 • Giorgio Angelotti, Nicolas Drougard, Caroline P. C. Chanel
Learning a Markov Decision Process (MDP) from a fixed batch of trajectories is a non-trivial task whose outcome's quality depends on both the amount and the diversity of the sampled regions of the state-action space.
1 code implementation • 27 May 2021 • Giorgio Angelotti, Nicolas Drougard, Caroline Ponzoni Carvalho Chanel
In Offline Model Learning for Planning and in Offline Reinforcement Learning, the limited data set hinders the estimate of the Value function of the relative Markov Decision Process (MDP).
no code implementations • 5 Oct 2020 • Giorgio Angelotti, Nicolas Drougard, Caroline Ponzoni Carvalho Chanel
Offline learning is the area of machine learning concerned with efficiently obtaining an optimal policy with a batch of previously collected experiences without further interaction with the environment.