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.

翻译：迁移学习是一种模型/智能体利用从某任务中获取的知识/专长来解决另一密切相关任务的技术，常用于深度学习中的问题处理。通过本项目，我们探索了深度强化学习领域中的迁移学习。具体而言，我们希望通过迁移学习实现OpenAI赛车环境中的快速圈速：在一个赛道训练智能体，并通过零样本迁移或额外微调在其他定制化目标环境中进行竞赛。此外，我们比较了基于模型与无模型方法的性能，并观察到在此环境中，基于模型的方法在性能上占优且收敛速度更快。我们发现，在大多数设置下，迁移学习不仅能提升目标域的性能，还能在学习过程中展现出强大的能力表现。