4 citations · 4 across the 1 of their papers we have counts for
3 papers
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act
Alexis Jacq, Johan Ferret, Olivier Pietquin +1
Traditionally, Reinforcement Learning (RL) aims at deciding how to act optimally for an artificial agent. We argue that deciding when to act is equally important. As humans, we dri…
Foolproof Cooperative Learning
Alexis Jacq, Julien Perolat, Matthieu Geist +1
This paper extends the notion of learning equilibrium in game theory from matrix games to stochastic games. We introduce Foolproof Cooperative Learning (FCL), an algorithm that con…
Cognitive Architecture for Mutual Modelling
Alexis Jacq, Wafa Johal, Pierre Dillenbourg +1
In social robotics, robots needs to be able to be understood by humans. Especially in collaborative tasks where they have to share mutual knowledge. For instance, in an educative s…