- title
- Autonomous robot car with Deep Reinforcement Learning
- Please use the following URL when quoting:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-945829
- publication_date
- 2024
- Date of submission
- 29.10.2024
- Date of defense
- 27.11.2024
- Abstract (EN)
- With the rapid development of Deep Reinforcement Learning (DRL), it has been widely applied in the field of autonomous driving (AD). This thesis presents an end-to-end approach based on the Proximal Policy Optimization (PPO) algorithm to achieve multi-tasks AD, including lane keeping and obstacle avoidance. To address the challenge of multi-tasks AD, a two-stage reinforcement learning (RL) strategy is adopted for both lane keeping and obstacle avoidance tasks, and its performance is compared with a RL baseline. Reward function is critical in RL. In this study, a suitable reward function is first identified through a non-end-to-end approach, as the non-end-to-end method converges faster. This reward function is then used to train the end-to-end lane keeping task. For obstacle avoidance, we propose three reward functions based on different principles. Each reward function is evaluated using both two-stage RL and normal RL, and the final comparison focuses on the obstacle avoidance performance of the three reward functions using the two-stage RL approach.
- Keywords (EN)
- Deep Reinforcement Learning, Autonomous driving, Proximal Policy Optimization, Lane keeping, Obstacle avoidance.
- Classification (DDC)
- 380
- Classification (RVK)
- ZO 4660
- Examiner
- Prof. Dr. Ostap Okhrin
- Prof. Dr. Georg Hirte
- supervisor
- M.Sc Dianzhao Li
- Awarding institution
- Technische Universität Dresden, Dresden
- version
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-945829
- Qucosa date of publication
- 02.12.2024
- Document type
- master_thesis
- Document language
- English
- licence
CC BY 4.0