Renault Software Labs

The project: Probabilistic Reinforcement Learning

In this project, the focus is on the development of new methodologies to enable decision components to operate an Autonomous Vehicle (AV), based on its current understanding of the surrounding environment, while taking into account the probabilistic (and thus uncertain) nature of the problem, such that safety considerations and objectives can be systematically fulfilled. In other words, the focus of this project is on the topic of probabilistic reinforcement learning: as a branch of machine learning, reinforcement learning (RL) is a computational approach to learning from interactions with the surrounding world and is concerned with sequential decision making in unknown environments to achieve high-level goals. Usually, no sophisticated prior knowledge is available and all required information to achieve the goal has to be obtained through trials. Since RL is inherently based on collected experience, it provides a general, intuitive, and theoretically powerful framework for autonomous learning and sequential decision making under uncertainty.

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Pietro Michiardi
Professor of Computer Science

My research interests include stochastic optimization, Bayesian inference, distributed algorithms.