智能驾驶强化学习算法科学家_CR
你的任务
- 针对端到端和 VLA 自动驾驶模型，开展强化学习算法研究与创新。
- 探索先进的强化学习方法（如策略优化、在离线 RL、层次化 RL、多智能体 RL），提升算法的鲁棒性和泛化能力。
- 探索先进的强化学习方法在自动驾驶领域的应用和拓展。
- 设计评测基准，并在复杂动态驾驶场景中开展实验验证。
- 与感知、规划、仿真团队协作，将 RL 方法集成到端到端自动驾驶训练框架中。
- 形成研究成果，撰写技术文档或对外发表技术报告。
- Research and develop novel reinforcement learning algorithms for end-to-end autonomous driving and VLA (Vision-Language-Action) models.
- Explore advanced RL techniques (e.g., policy optimization, online/offline RL, hierarchical RL, multi-agent RL) to improve robustness and generalization.
- Familiar advanced RL algorithm(e.g. GRPO, GSPO etc.) to improve autonomous driving.
- Design benchmarks and conduct experiments to evaluate algorithm performance in dynamic driving environments.
- Collaborate with perception, planning, and simulation teams to integrate RL methods into E2E autonomous driving pipelines.
- Publish technical reports and document research findings.
您的个人资料 1. 计算机、机器学习、自动化、机器人等相关专业硕士或博士学历。
2. 深入理解强化学习、深度学习与控制理论。
3. 熟悉 PyTorch/TensorFlow 及常用 RL 框架（如 RLlib、Stable-Baselines3）。
4. 在自动驾驶、机器人或多智能体系统方向有研究经验者优先。
5. 具备扎实的 Python/C++ 编程能力，熟悉大规模训练。
6. 具备较强的分析与问题解决能力，能够独立开展研究。
7. 具备良好的英文读写能力。
1. Ph.D. degree in Computer Science, Machine Learning, Robotics, or related fields.
2. Strong knowledge of reinforcement learning, deep learning, and control theory.
3. Hands-on experience with PyTorch/TensorFlow and common RL libraries (e.g., RLlib, Stable-Baselines3, etc.).
4. Research background in autonomous driving, robotics, or multi-agent systems is highly preferred.
5. Solid programming skills in Python/C++, good understanding of large-scale training.
6. Strong analytical and problem-solving skills; ability to work independently.
7. English reading/writing proficiency.
1. 计算机、机器学习、自动化、机器人等相关专业硕士或博士学历。
2. 深入理解强化学习、深度学习与控制理论。
3. 熟悉 PyTorch/TensorFlow 及常用 RL 框架（如 RLlib、Stable-Baselines3）。
4. 在自动驾驶、机器人或多智能体系统方向有研究经验者优先。
5. 具备扎实的 Python/C++ 编程能力，熟悉大规模训练。
6. 具备较强的分析与问题解决能力，能够独立开展研究。
7. 具备良好的英文读写能力。
1. Ph.D. degree in Computer Science, Machine Learning, Robotics, or related fields.
2. Strong knowledge of reinforcement learning, deep learning, and control theory.
3. Hands-on experience with PyTorch/TensorFlow and common RL libraries (e.g., RLlib, Stable-Baselines3, etc.).
4. Research background in autonomous driving, robotics, or multi-agent systems is highly preferred.
5. Solid programming skills in Python/C++, good understanding of large-scale training.
6. Strong analytical and problem-solving skills; ability to work independently.
7. English reading/writing proficiency.
