Master Thesis Hierarchical Reinforcement Learning Approach for Computational Resource Allocation of Concurrent Control Applications
Master Thesis Hierarchical Reinforcement Learning Approach for Computational Resource Allocation of Concurrent Control Applications
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Job Description
In modern control systems multiple control applications often operate simultaneously on a shared computational platform. The available computational resources however may be limited or costly necessitating the need for an efficient resource allocation. By strategically degrading some control applications the resource constraints can be managed at the cost of some accuracy. Recently the problem has been tackled with various optimization and heuristic techniques aimed at maximizing the control performance within the available resource constraints. The aim of this thesis would be learning a more efficient resource allocation policy by developing a Reinforcement Learning (RL) agent.
- During your thesis you will develop and implement a Reinforcement Learning (RL) agent for optimal resource allocation in advanced control applications deployed on a shared platform with limited computational resources.
- You will conduct a state-of-the-art analysis to frame the topic in the existing control systems literature.
- Furthermore you will translate the elements of the control framework into state space action space and reward function of the RL agent.
- Finally you will test and demonstrate the approach by simulation in a relevant industrial usecase.
Qualifications :
- Education: Master studies in the field of Control Engineering Robotics Mathematics Computer Science or comparable
- Experience and Knowledge: solid knowledge of control systems and optimization; coding experience in Python; familiarity with machine learning; experience with frameworks such as PyTorch TensorFlow
- Personality and Working Practice: you collaborate effectively work autonomously and systematically meticulously document your processes and clearly present your results
- Languages: very good in English
Additional Information :
Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.
Need further information about the job
Marcello Domenighini (Functional Department)
#LI-DNI
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
