Any tutorial on how to create RL C++ environments?

• open_spiel

  44 3,983 9.4 C++

  OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

  

  It's not exactly a tutorial, but OpenSpiel has C++ environments ported to Python that are relatively simple and easy to understand. Procgen would be a more complicated reference to check out as well.

• procgen

  3 974 0.0 C++

  Procgen Benchmark: Procedurally-Generated Game-Like Gym-Environments

  

  It's not exactly a tutorial, but OpenSpiel has C++ environments ported to Python that are relatively simple and easy to understand. Procgen would be a more complicated reference to check out as well.

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• ReinforcementLearning.jl

  2 564 8.8 Julia

  A reinforcement learning package for Julia

  

  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

  124 9,404 9.9 Python

  NumPy aware dynamic Python compiler using LLVM

  

  If you are more familiar with Python, I highly recommend trying out Numba, before going down the rabit hole of making c++ bindings.

• RustyNEAT

  2 0 7.8 Rust

  Discontinued Rust implementation of NEAT algorithm (HyperNEAT + ES-HyperNEAT + NoveltySearch + CTRNN + L-systems)

  If you want to really speed up your environment several orders of magnitude, you can implement it in cuda/vulkan /opencl. Here is an example of what I did in vulkan https://mobile.twitter.com/MendozaDrosik It allows me to stimulate thousands of agents in parallel. Works wonders especially if you want to use genetic algorithms. If you're interested, I might make python bindings to my minecraft environment. If you write in rust (like I do), then you can add python bindings very easily with PyO3. This is what I did here https://github.com/aleksander-mendoza/RustyNEAT/blob/main/rusty_neat_quick_guide.py (it's GPU accelerated implementation of NEAT algorithm)

• brax

  11 2,051 5.2 Jupyter Notebook

  Massively parallel rigidbody physics simulation on accelerator hardware.

  

  If you want raw speed, parallel execution on GPU or TPU is best. Checkout out our Brax simulator, which uses the XLA compiler and JAX Python frontend: https://github.com/google/brax

• tiny-differentiable-simulator

  6 1,147 1.6 C++

  Tiny Differentiable Simulator is a header-only C++ and CUDA physics library for reinforcement learning and robotics with zero dependencies.

  

  Or our C++ and CUDA Tiny Differentiable Simulator: https://github.com/google-research/tiny-differentiable-simulator

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