- 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.
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