做强化学习或自动驾驶的团队可以看看这篇——迁移学习让赛车智能体快速适应新赛道，模型方法收敛更快，值得一试。
该研究探索了深度强化学习中迁移学习的应用，通过在OpenAI赛车环境中训练智能体在一个赛道上，然后通过零样本迁移或微调在其他定制赛道上实现快速圈速。研究发现，基于模型的方法在性能和收敛速度上均优于无模型方法。迁移学习不仅提升了目标域的性能，还在学习过程中表现出高效性。
Transfer Learning for Customized Car Racing Environments
Transfer Learning, a technique where a model/agent can use the knowledge/expertise that it gained from one task and exploit that to solve another closely-related task, is often used in tackling problems in deep learning. Through this project, we explore transfer learning in the purview of deep reinforcement learning. Specifically, we want to use transfer learning to achieve the fast lap times in OpenAI's Car racing environment by training the agent on one circuit, and racing it on other customized target environments by zero-shot transfer or by additional fine-tuning. In addition, we compare the performance of model-based and model-free approaches, and observe that model-based approaches dominate in performance and converge faster than model-free approaches in this environment. We observe that transfer learning in most setups not only boosts the performance on the target domain, but also shows high performance ability during learning.