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Reinforcement Learning Resources
2020-10-30
I'll constantly post resources of
reinforcement learning (RL) here.
The focus will be on deep
reinforcement learning methods.
[Updated on November 20, 2020:
Add description.]
General
- awesome-rl
by dbobrenko is a repository of
RL related resources grouped by
RL sub-domains.
- awesome-rl
by aikorea is another repository
of RL related resources grouped
by resource type.
Books
Key Papers
- Key
Papers in Deep RL by
OpenAI is a list of must-read
papers of classic RL algorithms
selected by OpenAI researchers.
- Deep
Reinforcement Learning
by Yuxi Li is a comprehensive
and up-to-date RL survey paper.
It can also serve as a tutorial
for people who want to have a
general understanding of the
field.
Courses
- CS285 Deep Reinforcement
Learning at UC Berkeley by
Professor Sergey Levine is the
latest deep RL course. It covers
more recent topics and delves
deeper into each of them, so it
might be difficult for people
who are new to RL. [Course
website] [Playlist]
- Introduction to Reinforcement
Learning with David Silver by
David Silver is an introductory
RL course, which can be served
as a course for beginners in RL.
[Course
website] [Playlist]
Blog Posts
- A
(Long) Peek into
Reinforcement Learning
by Lilian Weng is a good blog
post for beginners in RL. For
most of the algorithms, it can
give you a high-level intuition
to help you with further
systematic study.
Tutorials
- pytorch-rl
by bentrevett is a practical
introduction to RL using
PyTorch.
- OpenAI
Spinning Up by OpenAI
might be the best educational
resource to start with in deep
RL. It covers key concepts in
RL, kinds of RL algorithms, and
a tutorial to the policy
gradient algorithm. It also
provides a resource list and
algorithm documentations.
Frameworks
- OpenAI
Gym by OpenAI is a
toolkit for benchmarking RL
algorithms.
Miscellaneous
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