
RLgraph: Robust, incrementally testable reinforcement learning - k_f
https://rlgraph.github.io/rlgraph/2019/01/04/introducing-rlgraph.html
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RSchaeffer
This is going to sound cynical, but I recently invested a week in rllib for a
project before discovering that much of the under-the-hood implementation was
horribly confusing, poorly documented and missing critical functionality (for
instance, their IMPALA implementation only works with discrete action spaces).
Does this library conceal similar problems?

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codesourcerer
Regarding our IMPALA implementation: It currently also only supports discrete
actions. However, our SAC algo is extremely strong. It learned (continuous)
Pendulum-v0 within only a few dozen episodes, so you could try that one
instead. As for ease of use: We believe our code is quite user friendly (take
a look at our example scripts and configs) and also well extendable due to the
strictly enforced modularity of our components and our abstract data flow
definitions inside an algorithm.

~~~
sirthomasjames
I also found extremely hard to understand and extend the under-the-hood
implementation. Couldn't grasp how the separation of concerns was split
between the different classes. The documentation is lacking examples on how
one of the algorithms (e.g. IMPALA, SAC, PPO) was built from scratch.

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voidmain
Regarding testability, what kinds of properties do the tests actually assert?

