2 citations · 2 across the 3 of their papers we have counts for
11 papers · 1 filter
Bridging the gap between Markowitz planning and deep reinforcement learning
Eric Benhamou, David Saltiel, Sandrine Ungari +1
While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum vari…
AAMDRL: Augmented Asset Management with Deep Reinforcement Learning
Eric Benhamou, David Saltiel, Sandrine Ungari +2
Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations? Through trading bots, we illustrate how De…
Estimating Individual Treatment Effects through Causal Populations Identification
Céline Beji, Michaël Bon, Florian Yger +1
Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one…
Variance Reduction in Actor Critic Methods (ACM)
Eric Benhamou
After presenting Actor Critic Methods (ACM), we show ACM are control variate estimators. Using the projection theorem, we prove that the Q and Advantage Actor Critic (A2C) methods…
NGO-GM: Natural Gradient Optimization for Graphical Models
Eric Benhamou, Jamal Atif, Rida Laraki +1
This paper deals with estimating model parameters in graphical models. We reformulate it as an information geometric optimization problem and introduce a natural gradient descent s…
Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)
Eric Benhamou
Reinforcement learning (RL) is about sequential decision making and is traditionally opposed to supervised learning (SL) and unsupervised learning (USL). In RL, given the current s…