ReinforcementLearning.jl
Numba
ReinforcementLearning.jl | Numba | |
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2 | 124 | |
566 | 9,452 | |
1.6% | 1.1% | |
8.7 | 9.9 | |
15 days ago | 7 days ago | |
Julia | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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ReinforcementLearning.jl
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What framework would you recommend to build a Tetris game AI using reinforcement learning?
I has a look to Julia too. There are nice tools build by JuliaDynamics. I.e. Agents.jl for agent based modeling. It handles collisions. There is also a framework for reinforcement learning. Also for Genetic Algorithms. Then I found a set of libraries related to Geometry. But it seems to be a lot of work to put that together for my use case.
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Any tutorial on how to create RL C++ environments?
And I know it's another language, but Julia has made significant strides in their RL packages and are pretty easy to integrate with Python
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
Agents.jl - Agent-based modeling framework in Julia
NetworkX - Network Analysis in Python
NetLogo - turtles, patches, and links for kids, teachers, and scientists
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
tiny-differentiable-simulator - Tiny Differentiable Simulator is a header-only C++ and CUDA physics library for reinforcement learning and robotics with zero dependencies.
Dask - Parallel computing with task scheduling
procgen - Procgen Benchmark: Procedurally-Generated Game-Like Gym-Environments
cupy - NumPy & SciPy for GPU
RustyNEAT - Rust implementation of NEAT algorithm (HyperNEAT + ES-HyperNEAT + NoveltySearch + CTRNN + L-systems)
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
brax - Massively parallel rigidbody physics simulation on accelerator hardware.
SymPy - A computer algebra system written in pure Python