Machine Learning for Simulated Military Vehicles
| Thesis title in Czech: | Strojové učení pro simulovaná vojenská vozidla |
|---|---|
| Thesis title in English: | Machine Learning for Simulated Military Vehicles |
| Key words: | Umělá Inteligence|Strojové Učení|Navigace|Simulace |
| English key words: | Artificial Intelligence|Machine Learning|Navigation|Simulation |
| Academic year of topic announcement: | 2018/2019 |
| Thesis type: | diploma thesis |
| Thesis language: | angličtina |
| Department: | Department of Software and Computer Science Education (32-KSVI) |
| Supervisor: | Mgr. Jakub Gemrot, Ph.D. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 08.01.2019 |
| Date of assignment: | 08.01.2019 |
| Confirmed by Study dept. on: | 17.01.2019 |
| Date and time of defence: | 02.09.2021 09:00 |
| Date of electronic submission: | 22.07.2021 |
| Date of submission of printed version: | 22.07.2021 |
| Date of proceeded defence: | 02.09.2021 |
| Opponents: | doc. Mgr. Martin Pilát, Ph.D. |
| Guidelines |
| Investigate how machine learning and artificial intelligence techniques can be applied to create a controller for a physically simulated military vehicle in a real world-like 3D virtual environment.
The vehicle should be able to navigate safely to a specified position. Present the results visually. The thesis may result in better and easier to develop virtual military vehicle controllers in simulators and computer games by replacing hand-crafted controllers. |
| References |
| Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., Schmidhuber, J. (2014). Natural evolution strategies. Journal of Machine Learning Research, 15(1), 949-980. Retrieved from http://www.jmlr.org/papers/volume15/wierstra14a/wierstra14a.pdf
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O. (2017). Proximal Policy Optimization Algorithms. Retrieved from: https://arxiv.org/abs/1707.06347 Salimans, T., Ho, J., Chen, X. Sutskever, I. (2017). Evolution Strategies as a Scalable Alternative to Reinforcement Learning. Retrieved from https://arxiv.org/pdf/1703.03864.pdf Ha, D., Schmidhuber, J. (2018). World Models. Retrieved from: https://arxiv.org/abs/1803.10122 |