semvec/pypi-packages huggingface dataset:
tc2 0.0.1594993188
A fast and easy way to run StarCraft: Brood War as a Gym environment.
License: MIT
Requires Python: >=3.6

# TorchCraft 2 [![CircleCI](https://circleci.com/gh/fairinternal/TorchCraft2.svg?style=svg&circle-token=6181f15b3ed51c760ace7cfdefda6f8a18dc5c1b)](https://circleci.com/gh/fairinternal/TorchCraft2)
### *(aka StarCraft Gym)*

The fastest, easiest way to use *StarCraft* as a reinforcement learning environment.

And all you have to do to get started is `pip install` it.

## Motivation

Researchers are eager to use *StarCraft*-based environments for RL experiments. But the process of getting *StarCraft* up and running can be time-consuming, and most environments aren't easy to use out of the box.

TorchCraft 2 offers simple (one-touch!) installation, an easy-to-use Gym interface, and multiple challenges/baselines out of the box.

In addition, we've worked with Blizzard to allow to TorchCraft 2 to include *first* legal distribution of *StarCraft: Brood War* binaries. This will enable the public to use TorchCraft 2 without having to acquire their own copies of *StarCraft*, or having to install it.

See the [project proposal](https://fb.quip.com/kzrHA90kEEMY) for details.

## Roadmap

With those ready, we're aiming to launch TorchCraft 2 internally for experiments and beta testing. From there, the plan is:
* Integration with TorchCraft (deprecating the original Lua/Python APIs)
* Pre-compiled binary distribution on PyPy
* Public release, PR, and tutorial content

The full roadmap lives [on GitHub](https://github.com/fairinternal/FAIR_rush/projects/21)

## Usage
### Building TorchCraft 2 for development

This is how I recommend using TorchCraft 2 at the moment:

```
git clone --recursive https://github.com/fairinternal/TorchCraft2/ tc2
cd tc2

conda create --name tc2 python=3 pip
source activate tc2
conda install pip cmake pybind11 numpy
conda install -y -c conda-forge sdl2 zstd
conda install -y -c anaconda zeromq
pip install -e .
```

### Downloading required Starcraft files
Once you have installed `tc2`, you have to download StarCraft data files (*MPQ files*). We provide you with
a tool to do that just for you - we can't provide them right away because we need you to read and accept Blizzard's EULA first.

```
tc2-setup
```


### Demo

This demo (`run_demo.py`) creates a StarCraft gym environment and controls an agent using a simple "attack the middle" policy.

```
import gym
import tc2
from tc2.agents import attack_middle

env = gym.make('tc2-demo-v0')
while True:
  env.reset()
  actions = []
  done = False
  while not done:
    observation, reward, done, info = env.step(actions)
    actions = attack_middle(observation)
```

### Running the TorchCraft 2 demo:

* `python3 run_demo.py`
pycuGMRES 1.1.4.6.9
Fast CUDA C++ GMRES implementation for Toeplitz-like (Toeplitz, Hankel, Circulant) matrices and mixed (combinations of Diagonal ones and Toeplitz-like ones) matrices.
License: MIT

# pycuGMRES

GMRES implementation for Toeplitz-like (Toeplitz, Hankel, Circulant) matrices and mixed (combinations of Diagonal ones and Toeplitz-like ones) matrices

We propose implementations of the Generalized Minimal Residual Method (GMRES) for solving linear systems based on dense, Toeplitz or mixed matrices. The software consists of a python module and a C++ library. The mixed matrices consist of the sum of the diag- onal and the Toeplitz matrices. The GMRES solver is parallelized for running on NVIDIA GPGPU accelerator. We report on the efficiency of the parallelization method applying GMRES to the Helmholtz linear system based on the use of Green’s Function Integral Equation Method (GFIEM) for computing electric field distribution in the design domain.