Read an article about the 'use-it-or-lose-it' nature of learning and realized I haven't really learned anything truly new in the 6 years since leaving undergrad. To remedy this, I decided to embark on an ML journey in my spare time. I'm still a code-monkey at work (slowly losing my knowledge of anything but Hack and React LOL), but I'd like to do this sort of stuff as a hobby instead of spending my weekends watching the same shows on Netflix. At this point, I'm just getting into Q-learning/DQN/RL. The way I normally learn all sorts of shit like this is by Googling [good university name] + [topic], going to the university's course website, and then going to the lecture videos, which are usually unlisted playlists on YouTube. In this case, however, I am so far out of my element that I can't tell which courses are good vs. bad. I took a look at Berkeley's (go Bears!) and found the lectures okay, but a bit dry. Can any recent grads from anywhere in the world recommend a good RL class they took that has its lectures online? Bonus points if it's taught in Python at any point, though I understand many of these are just the theory of it, which is fine. I'd appreciate it very much! #reinforcementlearning #machinelearning #ML #RL
OpenAI has a bunch of cool resources for RL on their website. They even have the barebones code for some common RL algos like PPO , DDPG, AC etc. You can read papers and implement new algos on top of what's already provided and see your model convergence in action. Check out their MuJoCo env for starters. Rich Sutton has an RL series on Coursera for what's it worth, but I found that I learnt the most from OpenAIs projects and blogs
I liked the Sutton and Barto Reinforcement learning textbook Pair it with some DQN projects and you should be good
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How good is your mathematical background? I have a PhD so my recommendations may not be suitable.
Open AI RL blog
What’s your recommendation?