Reinforcement Learning (RL)  is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment.
RL together with Deep Learning has obtained excellent results in a great number of simulated environments, like video-games  or board-games . This algorithms are very promising for industrial applications which involve optimal control.
The most Deep Reinforcement Learning (DRL) algorithms developed so far are model-free that is, they do not use a model of the environment during the training phase. Starting from the Dyna-Q tabular algorithm , the research community has tried to speed up RL training introducing a model of the environment. The model allows to reuse multiple times the experience acquired during the agent-environment interaction and it improves the agent’s actions with respect to the control objective. Recently proposed solutions merge the ideas of DRL with model-based RL [4, 5].
The objective of the thesis is to study the possibility to develop existing model-free DRL algorithms by incorporating a model into the training phase.
The model introduce a computational overhead that has to be quantified with respect to the training speed-up. Therefore, an extended comparison between model-based and model-free has to inquire the cost-benefit impact of the model itself.
- Acquire strong theoretical basis on Deep Reinforcement Learning;
- Investigate the different possibilities to integrate a model into an existing model-free DRL algorithm;
- Compare different pairs model-free and model-based algorithms finding the break-even value from the points of view of computational overhead and training speed-up.
Competencies to be acquired
The candidate will acquire:
- Expertise on recent Deep Reinforcement Learning algorithms;
- Experience in algorithm design, analysis and comparison with respect to a real application;
- Experience of data-driven modelling in the field of physical systems.
Duration of this Project: 5-6 months.
Who we’re looking for
Students that are about to get their Master Degree in: computer science, computer engineering, mechatronic engineering, electronic engineering, aerospace engineering, mathematical engineering, mathematics, physics, physics of complex systems.
- Proficiency in at least one programming language (Python, Lua, Matlab, C++, Java);
- Basic knowledge of machine learning, in particular supervised learning;
- Good knowledge of linear algebra.
- Basic knowledge of dynamical system modelling