PhD thesis - Paris-Saclay - Reinforcement Learning and statistical signal processing for surveillance
3 Juin 2022
Catégorie : Doctorant
Submission deadline: 04/09/2022
Starting date : 01/10/2022
Ending date : 30/09/2025
- Lab : Laboratoire des signaux et systèmes (L2S), Université Paris-Saclay/CentraleSupélec, 3 rue Joliot Curie, 91192 Gif-sur-Yvette, France
- Supervisors : Stefano Fortunati (email@example.com), Alexandre Renaux (firstname.lastname@example.org)
- Starting date : 01/10/2022
- Duration : 3 years
- Gross Salary : 2150€ monthly (including 64 hours of teaching duty per year at IPSA)
- Title : Non-Stationary and robust Reinforcement Learning methodologies for surveillance applications
- Keywords : Markov decision process, Reinforcement Learning (RL), Multi-agent RL, Non-Stationary Stochastic Learning, Robust Statistics.
- Summary of thesis project : One of the underlying assumptions of Reinforcement Learning (RL) methodologies is the stationarity of the environment embedding the agent. Specifically, the three main elements characterizing a Markov Decision Process (MDP), i.e. the set of states, the set of actions and the reward function are assumed to be constant/invariant over time. In surveillance applications however, such assumption is generally unrealistic since the environment (i.e. the area that the agent has to monitor) is constantly changing. The amount and types of objects or different disturbance statistics are just two examples of non-stationarity. The main aim of this project is then the development of original RL schemes able to cope with time-dependent MDP. This challenging goal may bring significant benefits in many theoretical and applicative AI-subfield: from the statistical learning theory, non-stationary random processes and sets to Signal Processing applications. The theoretical findings will be validated in an emerging crucial issue: the detections of drones using massive antenna arrays.
- Profile : This interdisciplinary project requires skills in statistical signal processing and machine learning, with specifical focus on Reinforcement Learning. Basic knowledge of radar principles may be useful but not required. Concerning the programming languages, the candidate should have a good knowledge of Matlab and possibly of Python.
Additional information can be found here: