#168 overall
CS 294 Deep Reinforcement Learning
UC Berkeley
2 recommenders · 3 threads · recommended in 2 years, 2017–2019 · score 2.7
CS 294 Deep Reinforcement Learning is a UC Berkeley graduate course covering deep RL methods such as policy gradients, actor-critic, Q-learning and model-based RL, with lecture videos, slides and homework assignments. The course site is hosted by Berkeley's RAIL lab.
Commenters call it a great, comprehensive, up-to-date lecture course taught by Sergey Levine, one of the researchers they consider foundational in deep RL.
Machine Learning & AIadvancedfreevideo lectures#deep reinforcement learning#policy gradients#q-learning#levine#berkeley
Recommendations by year
20062026
If you're looking for a great lecture course, I highly recommend CS 294. It's taught by Sergey Levine, who is in my opinion one of the foundational researchers at the frontier of deep reinforcement learning, and provides a comprehensive overview on the current state of the art: http://rail.eecs.berkeley.edu/deeprlcourse/
CS 294 is a great deep reinforcement learning course that I highly recommend [1]. Sergey Levine is god tier RL pretty much.
[1] http://rail.eecs.berkeley.edu/deeprlcourse/
You should check out Berkeley's deep reinforcement learning course[1]. There's lecture videos, slides, and homework assignments, and it's all very up-to-date.
[1] http://rll.berkeley.edu/deeprlcourse/
I know this is for MOOCS but there are lots of great courses that are available online from universities where this would be useful. I'm taking Deep Reinforcement Learning (CS294) from Berkeley. Adding the option for courses like that would be really helpful!
Greg Brockman (Founder of Open AI) has written this amazing answer:
If you want to read one main resource... the Goodfellow, Bengio, Courville book (available for free from http://www.deeplearningbook.org/) is an extremely comprehensive survey of the field. It contains essentially all the concepts and intuition needed for deep learning engineering (except reinforcement learning).
If you'd like to take courses... Pieter Abbeel and Wojciech Zaremba suggest the following course sequence:
- Linear Algebra — Stephen Boyd’s EE263 (Stanford) - Neural Networks for Machine Learning — Geoff Hinton (Coursera) - Neural Nets — Andrej Karpathy’s CS231N (Stanford) - Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287 (Berkeley) - Deep RL — John Schulman’s CS294-112 (Berkeley)
(Pieter also recommends the Cover & Thomas information theory and Nocedal & Wright nonlinear optimization books).
If you'd like to get your hands dirty... Ilya Sutskever recommends implementing simple MNIST classifiers, small convnets, reimplementing char-rnn, and then playing with a big convnet. Personally, I started out by picking Kaggle competitions (especially the "Knowledge" ones) and using those as a source of problems. Implementing agents for OpenAI Gym (or algorithms for the set of research problems we’ll be releasing soon) could also be a good starting place.
Quora link: https://www.quora.com/What-are-the-best-ways-to-pick-up-Deep...