- Tytuł:
- Imitation learning of car driving skills with decision trees and random forests
- Autorzy:
-
Cichosz, P.
Pawełczak, Ł. - Powiązania:
- https://bibliotekanauki.pl/articles/329901.pdf
- Data publikacji:
- 2014
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
imitation learning
behavioral cloning
model ensemble
random forest
control
autonomous driving
car racing
decision tree
drzewo decyzyjne
lasy losowe
sterowanie
wyścigi samochodowe - Opis:
- Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision tree knowledge representation is adopted, which combines potentially high accuracy with human readability, an advantage that can be important in many applications. Decision trees are demonstrated to be capable of representing high quality control models, reaching the performance level of sophisticated pre-designed algorithms. This is achieved by enhancing the basic imitation learning scenario to include active retraining, automatically triggered on control failures. It is also demonstrated how better stability and generalization can be achieved by sacrificing human-readability and using decision tree model ensembles. The methodology for learning control models contributed by this article can be hopefully applied to solve real-world control tasks, as well as to develop video game bots.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 579-597
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł