2 citations · 4 across the 5 of their papers we have counts for
5 papers
Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming
Giorgio Angelotti, Caroline P. C. Chanel, Adam H. M. Pinto +3
The integration of physiological computing into mixed-initiative human-robot interaction systems offers valuable advantages in autonomous task allocation by incorporating real-time…
Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning
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. Sometimes the d…
Expert-Guided Symmetry Detection in Markov Decision Processes
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 sample…
An Offline Risk-aware Policy Selection Method for Bayesian Markov Decision Processes
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 Proces…
Offline Learning for Planning: A Summary
Giorgio Angelotti, Nicolas Drougard, Caroline Ponzoni Carvalho Chanel
The training of autonomous agents often requires expensive and unsafe trial-and-error interactions with the environment. Nowadays several data sets containing recorded experiences…