End-to-end autonomous driving based on reinforcement learning
In this project, an RGB camera will be used as data input to explore an end-to-end method based on visual based reinforcement learning. The project will be carried out with the Unity game engine as the training environment, along with Unity’s ML-Agents package that provides out of the box deep Reinf...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158264 |
| _version_ | 1826118248588902400 |
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| author | Ong, Chee Wei |
| author2 | Lyu Chen |
| author_facet | Lyu Chen Ong, Chee Wei |
| author_sort | Ong, Chee Wei |
| collection | NTU |
| description | In this project, an RGB camera will be used as data input to explore an end-to-end method based on visual based reinforcement learning. The project will be carried out with the Unity game engine as the training environment, along with Unity’s ML-Agents package that provides out of the box deep Reinforcement Learning (RL) algorithms to interface with their environment. The results of training a simulated donkey car to drive in its own lane with an on-policy method, Proximal Policy Optimization (PPO), and an off-policy method, Soft Actor-Critic (SAC) will be compared. An ablation study, consisting of adding Generative Adversarial Imitation Learning (GAIL), semantic segmentation and stacked visual inputs, will be performed. Additionally, RL based obstacle avoidance will be explored. The results, based on stability of control and ability to stay in lane, indicate that the best performing method is PPO. Code is available at: https://github.com/MrOCW/Autonomous-Driving-RL-Unity |
| first_indexed | 2024-10-01T04:40:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158264 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:40:37Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1582642022-06-02T08:08:32Z End-to-end autonomous driving based on reinforcement learning Ong, Chee Wei Lyu Chen School of Mechanical and Aerospace Engineering lyuchen@ntu.edu.sg Engineering::Mechanical engineering::Mechatronics Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision In this project, an RGB camera will be used as data input to explore an end-to-end method based on visual based reinforcement learning. The project will be carried out with the Unity game engine as the training environment, along with Unity’s ML-Agents package that provides out of the box deep Reinforcement Learning (RL) algorithms to interface with their environment. The results of training a simulated donkey car to drive in its own lane with an on-policy method, Proximal Policy Optimization (PPO), and an off-policy method, Soft Actor-Critic (SAC) will be compared. An ablation study, consisting of adding Generative Adversarial Imitation Learning (GAIL), semantic segmentation and stacked visual inputs, will be performed. Additionally, RL based obstacle avoidance will be explored. The results, based on stability of control and ability to stay in lane, indicate that the best performing method is PPO. Code is available at: https://github.com/MrOCW/Autonomous-Driving-RL-Unity Bachelor of Engineering (Mechanical Engineering) 2022-06-02T08:08:32Z 2022-06-02T08:08:32Z 2022 Final Year Project (FYP) Ong, C. W. (2022). End-to-end autonomous driving based on reinforcement learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158264 https://hdl.handle.net/10356/158264 en C040 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Mechanical engineering::Mechatronics Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Ong, Chee Wei End-to-end autonomous driving based on reinforcement learning |
| title | End-to-end autonomous driving based on reinforcement learning |
| title_full | End-to-end autonomous driving based on reinforcement learning |
| title_fullStr | End-to-end autonomous driving based on reinforcement learning |
| title_full_unstemmed | End-to-end autonomous driving based on reinforcement learning |
| title_short | End-to-end autonomous driving based on reinforcement learning |
| title_sort | end to end autonomous driving based on reinforcement learning |
| topic | Engineering::Mechanical engineering::Mechatronics Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | https://hdl.handle.net/10356/158264 |
| work_keys_str_mv | AT ongcheewei endtoendautonomousdrivingbasedonreinforcementlearning |