Lucas Schott
Research Engineer Deep Reinforcement Learning (DRL) And Robustness Of Autonomous Agents at ISIR
Paris, Ile-de-France
Summary
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Senior🎓
Top SchoolLucas Schott is a research engineer and PhD candidate specializing in deep reinforcement learning and the robustness and safety of autonomous agents, based in Paris, France. He applies DRL to autonomous driving and hybrid DRL-plus-planning in simulation, with a focus on adversarial attacks and defense strategies to improve reliability. At IRT SystemX, he works on robustness and functional safety, developing adversarial attacks and defenses and integrating DRL with planning for railway and other defense-grade simulators. He is an active open-source contributor, notably contributing to HighwayEnv by enhancing its kinematic observation system and fixing observation logic bugs to improve agent perception. His academic path spans a PhD at Sorbonne University and MSc programs in data science, deep learning, and reinforcement learning, complemented by teaching roles at ENSAE Paris and Sorbonne University. He brings a rare blend of rigorous research, practical engineering, and cross-domain experience in NLP and multi-agent systems to tackle safety-critical AI challenges.
9 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at University of StrasbourgSorbonne University
Stackoverflow
1reputation
0reached
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Github Skills (7)
gymnasium10
environ10
python10
autonomous-driving10
reinforcement-learning10
enviroment10
pandas8
Programming languages (1)
Github contributions (5)
Farama-Foundation/HighwayEnv
May 2020 - Oct 2022
A minimalist environment for decision-making in autonomous driving
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits, 2 PRs, 8 comments in 2 years 5 months
Contributions summary:Lucas primarily focused on modifying the `highway_env` environment, specifically the kinematic observation system. Their contributions involved adding, modifying, and removing parameters related to vehicle observations. They addressed bugs related to observation logic and the `close_vehicles_to` function, refining the observation process and fixing conditional statements. These changes directly impacted how the environment perceives and processes information about vehicles, which is crucial for reinforcement learning agents.
pytorchdecision-makingautonomousdecisionminimalist
lucasschott/highway-env
May 2020 - Oct 2022
An environment for autonomous driving decision-making
Contributions:1 release, 30 pushes, 3 branches in 2 years 5 months
pytorchdecision-makingautonomousdecisionreinforcement-learning
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