78 citations · 91 across the 3 of their papers we have counts for
4 papers
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Jesper Klicks, Sander Vržina, Vincent François-Lavet
Energy trading decisions depend not only on current market prices, but also on expected future market conditions, and operational constraints. This makes the state representation g…
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
Vincent Francois-Lavet, Guillaume Rabusseau, Joelle Pineau +2
This paper provides an analysis of the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data) in the co…
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies
Vincent François-Lavet, Raphael Fonteneau, Damien Ernst
Using deep neural nets as function approximator for reinforcement learning tasks have recently been shown to be very powerful for solving problems approaching real-world complexity…
Simple connectome inference from partial correlation statistics in calcium imaging
Antonio Sutera, Arnaud Joly, Vincent François-Lavet +4
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, proces…