Logging my Reinforcement Learning journey
I have been procrastinating learning Reinforcement Learning (RL) since 2 years now. I think the real reason is there’s already so much to learn. So many conferences every other week. Their papers and every field is exciting in its own beautiful way. But here I finally got the chance to block some time off my calendar to learn some RL every week.
My current stage- I have an idea about what RL is (high level definition) and all the buzz words around it ( Like model free RL, exploration vs exploitation). I have never taken a course specifically to RL in school or online.
My aim at the end of this journey- Not being very ambitious about what I would like to achieve but at the least I want to be able to understand the current research taking place. In the industry side I would like to understand the products which are already using RL. I would also like to know the computational side of things like latest libraries pitfalls etc. If I am lucky I will make my own RL diagram (they are super cool) with an agent , environment, reward func etc!
If you want to continue this journey with me shoot me an email on firstname.lastname@example.org. Probably we can learn a thing or two from each other. Or you could tell me some better resources!
I have started with the most popular course on RL by David Silver. By the time I am writing this, I have already completed my first lecture. I knew most of things in it. I have taken a random processes class in my grad school which had markov processes as one of its major topics. Looking to the course (till now ) that knowledge might be helpful. Going pretty optimistic for now :)