Reinforcement Learning for Autonomous Aircraft Control and Aerial Maneuvering in Simulated Environments

This thesis explores reinforcement learning for autonomous airplane navigation in a Unity-based simulation. Using Proximal Policy Optimization (PPO), the agent learns to fly, avoid obstacles, and reach targets through continuous control. A combination of environment design and reward shaping was essential for achieving stable learning. Results show that performance improves significantly with training and proper reward design. Experiments across different difficulty levels highlight the impact of environment complexity on learning.

Department dc:contributor.department

DE--Informatikai Kar

Author dc:creator

• Shili, Mehdi

Advisor dc:contributor.advisor

• Bogacsovics, Gergő

• Unity
• Reinforcement learning
• Autonomous Aircraft Control

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2437/413148

OAI identifier oai:identifier

oai:dea.lib.unideb.hu:2437/413148