Reinforcement Learning Vs Transfer Learning?
I didn't watch this lecture, but, the way I see it, reinforcement learning and transfer learning are absolutely different things. Transfer learning is about fine-tuning a model, which was trained on one data and then striving to work with another data and another task. For example if you use weights of pretrained model on imagenet and then implement it to your dataset, while your dataset consists of small amount of different species of birds images (which might be not sufficient to train for example unet from a scrath). Reinforcement learning is about how some agent should response to environment condition to receive high reward. There is an illustrative example with a drone making a delivery, when there is some range of restrictions of the environment. There are two links, which might be useful:
https://machinelearningmastery.com/transfer-learning-for-deep-learning/
https://skymind.com/wiki/deep-reinforcement-learning

I guess, I can't answer, which approach is better, because they aim to solve different challenges.