Reinforcement Learning and Optimal Control
web.mit.edu
Reinforcement Learning and Optimal Control
1–10 of 14 posts
Re: Reinforcement Learning and Optimal Control
#2Seems like an interesting complement to the (free) book on planning algorithms by Steven Lavalle: http://planning.cs.uiuc.edu/
Re: Reinforcement Learning and Optimal Control
#3If 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/
Re: Reinforcement Learning and Optimal Control
#4Bertsekas' earlier books (Dynamic Programming and Optimal Control + Neurodynamic Programming w/ Tsitsiklis) are great references and collect many insights & results that you'd otherwise have to trawl the literature for.
I'm very interested to see what a book focused more narrowly on RL will be like-- Sutton's Introduction to Reinforcement Learning[0] is fantastic, but if you're going to do research on RL, another text such as this one is necessary.
Re: Reinforcement Learning and Optimal Control
#5This inspired me so much. Thank you for sharing!
Re: Reinforcement Learning and Optimal Control
#6If 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/
I took the time to look through two of the lectures from the slides available. This is so inspirational. Thanks a lot!
Re: Reinforcement Learning and Optimal Control
#7I liked the open courseware lectures with John Tsitsiklis and ended up with a few books by Bertsekas: neuro dynamic programming and intro probability. Both Bertsekas and Tsitsiklis recommended the Sutton and Barto intro book for an intuitive overview. I liked it.
Re: Reinforcement Learning and Optimal Control
#8Not meant as a critique to this book, but the fact that RL-based approaches rarely work for optimal control problems (in, for example, robotics) came as a surprise to me, given the hype and focus on RL [1]. It turns out that model-based methods for optimal control (e.g. linear quadratic control) invented quite a long time ago dramatically outperform RL-based approaches in most tasks and require multiple orders of magnitude less computational resources. Maybe there's some hope for RL method if they "course correct" for simpler control methods. At the moment, it seems like RL for robotics and control lies to the side of "research" and not "engineering."
Re: Reinforcement Learning and Optimal Control
#9Not meant as a critique to this book, but the fact that RL-based approaches rarely work for optimal control problems (in, for example, robotics) came as a surprise to me, given the hype and focus on RL [1]. It turns out that model-based methods for optimal control (e.g. linear quadratic control) invented quite a long time ago dramatically outperform RL-based approaches in most tasks and require multiple orders of mag…
for linear systems modern control theory is already optimal (?) so given a linear system I'd assume an RL method would just approximate the optimal control method. I think the potential for RL methods is in capturing nonlinearities in actuators like artificial muscle.
Re: Reinforcement Learning and Optimal Control
#10If 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/
Any idea how it compares to the UCL course taught by researchers from DeepMind?
http://www.cs.ucl.ac.uk/syllabus/compgi/compgi22_advanced_de...