A good reinforcement resource -

https://urldefense.proofpoint.com/v2/url?u=https-3A__web.stanford.edu_class_psych209_Readings_SuttonBartoIPRLBook2ndEd.pdf&d=DwICAg&c=jf_iaSHvJObTbx-siA1ZOg&r=y_b69HJLyjn8lFwzYVfyxol578OEO4exeFDpgGN6MoQ&m=DP4VAIUt9yAI5EgscfOsnowTXUt4aKes1rBKt9ZmEi8&s=TS7tm5uW4x_prNkeiXMr6vWH-MGIATcvOwyA9A5QMRE&e=

Back to reinforcement learning and discuss the difference between MC and TD - https://drive.google.com/file/d/1QDjeHLUEk0kL6fyxTE_DGANDcyLK5zF0/view?usp=sharing

There is a deep relation between re-enforcement learning and recurring games which we'll start exploring next - https://drive.google.com/file/d/10FF4i_uURnzfqt6XnjK2CDWRgtoWxw3s/view?usp=sharing

We'll continue with reinforcement learning https://drive.google.com/file/d/1xOytbzidb-oBRpRUqsfMmALQXpVkTcOs/view?usp=sharing and discuss how to learn the value of one policy by simulating another.