Inverse Reinforcement Learning for Autonomous Driving



Reinforcement Learning [1] (RL) is an emerging field of Artificial Intelligence (AI) that is giving extraordinary results in different applications.

One of such applications is Autonomous Driving, but to apply RL to this task an accurate choice of the reward function is needed. To overcome this issue, one solution is to infer the reward function applying Machine Learning (ML) techniques to some examples provided by experts. For example, a driver can show how to do a specific maneuver and a ML algorithm extract the objective function maximized by the driver behaviour. This method is known as Inverse Reinforcement Learning [2] (IRL).

The thesis will deepen the theory behind inverse reinforcement learning to analyze the possible applications of this approach to autonomous driving [3] in a simulated environment [4, 5, 6].

Planned Activities

  1. Acquire strong theoretical basis on Reinforcement Learning and in particular on Inverse Reinforcement Learning;
  2. Test the latest algorithms to “toy” environments;
  3. Adapt and test the most promising algorithms to a complex vehicle model.

Competencies to be acquired

The candidate will acquire:

  • Experience with the application of Machine Learning to complex systems.
  • Expertise on the most recent Reinforcement Learning algorithms;
  • Proficiency in the application of Inverse Reinforcement Learning algorithms to Autonomous Driving.

Duration of this Project: 5-6 months.

Who we’re looking for

Students that are about to get their Master Degree in: computer engineering, mechatronic engineering, aerospace engineering, automotive engineering, electronic engineering.

Required Skills:

Proficiency in at least one programming language (Python, Lua, Matlab/Simulink, C++, Java);

Basic knowledge of machine learning;

Good knowledge of linear algebra;

Basic knowledge of vehicle control systems and vehicle dynamics.

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Contact Us

Directly by email to: [email protected]

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