Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866914023138656256 |
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| author | Zhang, Zhihao Peng, Chengyang Yurtsever, Ekim Redmill, Keith A. |
| author_facet | Zhang, Zhihao Peng, Chengyang Yurtsever, Ekim Redmill, Keith A. |
| contents | Automated vehicle control using reinforcement learning (RL) has attracted significant attention due to its potential to learn driving policies through environment interaction. However, RL agents often face training challenges in sample efficiency and effective exploration, making it difficult to discover an optimal driving strategy. To address these issues, we propose guiding the RL driving agent with a demonstration policy that need not be a highly optimized or expert-level controller. Specifically, we integrate a rule-based lane change controller with the Soft Actor Critic (SAC) algorithm to enhance exploration and learning efficiency. Our approach demonstrates improved driving performance and can be extended to other driving scenarios that can similarly benefit from demonstration-based guidance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04712 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Zhang, Zhihao Peng, Chengyang Yurtsever, Ekim Redmill, Keith A. Robotics Artificial Intelligence Machine Learning Systems and Control Automated vehicle control using reinforcement learning (RL) has attracted significant attention due to its potential to learn driving policies through environment interaction. However, RL agents often face training challenges in sample efficiency and effective exploration, making it difficult to discover an optimal driving strategy. To address these issues, we propose guiding the RL driving agent with a demonstration policy that need not be a highly optimized or expert-level controller. Specifically, we integrate a rule-based lane change controller with the Soft Actor Critic (SAC) algorithm to enhance exploration and learning efficiency. Our approach demonstrates improved driving performance and can be extended to other driving scenarios that can similarly benefit from demonstration-based guidance. |
| title | Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving |
| topic | Robotics Artificial Intelligence Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2509.04712 |