42 citations · 121 across the 9 of their papers we have counts for
11 papers
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Daniel Guo, Bernardo Avila Pires, Bilal Piot +4
Learning a good representation is an essential component for deep reinforcement learning (RL). Representation learning is especially important in multitask and partially observable…
World Discovery Models
Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo Avila Pires +3
As humans we are driven by a strong desire for seeking novelty in our world. Also upon observing a novel pattern we are capable of refining our understanding of the world based on…
Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning
Guillaume Devineau, Philip Polack, Florent Altché +1
This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control…
Guaranteeing Consistency in a Motion Planning and Control Architecture Using a Kinematic Bicycle Model
Philip Polack, Florent Altché, Brigitte d'Andréa-Novel +1
This paper proposes to combine a 10Hz motion planner based on a kinematic bicycle Model Predictive Control (MPC) and a 100Hz closed-loop Proportional-Integral-Derivative (PID) cont…
An LSTM Network for Highway Trajectory Prediction
Florent Altché, Arnaud de La Fortelle
In order to drive safely and efficiently on public roads, autonomous vehicles will have to understand the intentions of surrounding vehicles, and adapt their own behavior according…
Partitioning of the Free Space-Time for On-Road Navigation of Autonomous Ground Vehicles
Florent Altché, Arnaud de La Fortelle
In this article, we consider the problem of trajectory planning and control for on-road driving of an autonomous ground vehicle (AGV) in presence of static or moving obstacles. We…