https://github.com/oolong-dev/oolong.jl.bak
I used to drink coffee a lot. But oolong has become my favorite recently. Hope you like it too.π«
https://github.com/oolong-dev/oolong.jl.bak
Last synced: about 2 months ago
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I used to drink coffee a lot. But oolong has become my favorite recently. Hope you like it too.π«
- Host: GitHub
- URL: https://github.com/oolong-dev/oolong.jl.bak
- Owner: oolong-dev
- License: mit
- Created: 2021-06-30T15:47:12.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2021-09-13T16:50:23.000Z (over 3 years ago)
- Last Synced: 2025-01-26T03:16:40.524Z (4 months ago)
- Language: Julia
- Homepage:
- Size: 10.7 KB
- Stars: 12
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Oolong.jl
*An actor framework for [ReinforcementLearning.jl](https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl)*
> βζ―ιζθ΄₯转倴空β ββ [γδΈ΄ζ±δ»γ](https://www.vincentpoon.com/the-immortals-by-the-river-----------------.html)
> [ζ¨ζ
](https://zh.wikipedia.org/zh-hans/%E6%9D%A8%E6%85%8E)
>
> "Success or failure, right or wrong, all turn out vain." - [*The Immortals by
> the
> River*](https://www.vincentpoon.com/the-immortals-by-the-river-----------------.html),
> [Yang Shen](https://en.wikipedia.org/wiki/Yang_Shen)
>
> (Translated by [Xu Yuanchong](https://en.wikipedia.org/wiki/Xu_Yuanchong))
## Roadmap
- [x] Figure out a set of simple primitives for running distributed
applications.
- [ ] Apply this package to some typical RL algorithms:
- [x] Parameter server
- [x] Batch serving
- [ ] Add macro to expose a http endpoint
- [ ] A3C
- [ ] D4PG
- [ ] AlphaZero
- [ ] Deep CFR
- [ ] NFSP
- [ ] Evolution algorithms
- [ ] Resource management across nodes
- [ ] State persistence and fault tolerance
- [ ] Configurable logging and dashboard
- [LokiLogger.jl](https://github.com/fredrikekre/LokiLogger.jl)
- [Stipple.jl](https://github.com/GenieFramework/Stipple.jl)
## Get Started
β *This package is still under rapid development and is not registered yet.*
First install this package:
```julia
pkg> activate --temp
pkg> add https://github.com/JuliaReinforcementLearning/Oolong.jl
```
`Oolong.jl` adopts the [actor model](https://en.wikipedia.org/wiki/Actor_model) to
parallelize your existing code. One of the core APIs defined in this package is
the `@actor` macro.
```julia
using Oolong
A = @actor () -> @info "Hello World"
```
By putting the `@actor` macro before arbitrary callable object, we defined an
**actor**. And we can call it as usual:
```julia
A();
```
You'll see something like this on your screen:
```
Info:[2021-06-30 22:59:51](@/user/#1)Hello World
```
Next, let's make sure anonymous functions with positional and keyword arguments
can also work as expected:
```julia
A = @actor (msg;suffix="!") -> @info "Hello " * msg * suffix
A("World";suffix="!!!")
# Info:[2021-06-30 23:00:38](@/user/#5)Hello World!!!
```
For some functions, we are more interested in the returned value.
```julia
A = @actor msg -> "Hello " * msg
res = A("World")
```
Well, different from the general function call, a result similar to `Future` is
returned instead of the real value. We can then fetch the result with the
following syntax:
```julia
res[]
# "Hello World"
```
To maintain the internal states across different calls, we can also apply `@actor`
to a customized structure:
```julia
Base.@kwdef mutable struct Counter
n::Int = 0
end
(c::Counter)() = c.n += 1
A = @actor Counter()
for _ in 1:10
A()
end
n = A.n
n[]
# 10
```
Note that similar to function call, the return of `A.n` is also a `Future` like object.
### Tips
- Be careful with `self()`
## Acknowledgement
This package is mainly inspired by the following packages:
- [Actors.jl](https://github.com/JuliaActors/Actors.jl)
- [Proto.Actor](https://proto.actor/)
- [Ray](https://ray.io/)