# CS285: Deep RL Notes For UC Berkeley CS285: Deep Reinforcement Learning, Decision Making, and Control, taught by Professor Sergey Levine. Official Course Website: [http://rail.eecs.berkeley.edu/deeprlcourse ](http://rail.eecs.berkeley.edu/deeprlcourse) These are my personal notes and wordy explanations on the core topics covered in this course, it’s meant to be a reference and sanity check for myself and for others learning deep RL. I typically write extensively about the most important parts of each lecture, so the notes are not (yet) comprehensive. I highly recommend watching the recorded lectures if you are self-studying for this class, professor Levine is a fantastic lecturer and many student questions that got recorded were also really interesting. All screenshots/images in these notes credit to CS285 lecture slides. I'll try to keep updating new topics whenever possible, please reach out to me to [mandi.zhao@berkeley.edu](https://mandizhao.github.io/cs285/mandi.zhao@berkeley.edu) if you’d like to contribute to writing or beautifying these notes! **Happy Reinforcement Learning! :)** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://mandi-zhao.gitbook.io/deeprl-notes/master.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.