semvec/pypi-packages huggingface dataset:
pokemonStreamTools-drk98 0.0.1
Tools for streaming pokemon
Requires Python: >=3.6

# Pokemon Stream Tools

To install:

`pip install pokemon-stream-tools`

## Current Programs

### Shiny Odds
This program will produce plot and text files for OBS that show the current odds* to have encountered a shiny mon.

To run:
```
usage: shinyOdds [-h] [--nShinies [NSHINIES]] Encounters.txt Odds

Time to shiny Hunt.

positional arguments:
  Encounters.txt        The file location for the number of encounters
  Odds                  The odds to find a shiny (i.e. 1/4096)

optional arguments:
  -h, --help            show this help message and exit
  --nShinies [NSHINIES]
                        The number of shinies you're hunting for
```

This will produce 3 files: a plot of your current shiny odds, a text file that has your current odds, and a text file that has the number of encounters until you reach your next percentage milestone. 
These files are located in the same directory as Encounters.txt.

*The proper phrasing is x% of people at N encounters have gotten a shiny. 

### Steam Ender
This program will auto end stream if Encounters.txt has not been updated in a certain time. 
Useful for when using automatic hunters and you do not want your stream to run hours after it is found. 

To run:
```

usage: streamEnder [-h] [--password [PASSWORD]] [--timeout [TIMEOUT]] [--EndStreamTime [ENDSTREAMTIME]]
                   [--EndStreamName ENDSTREAMNAME]
                   Encounters.txt OBS_IP PORT

Time to shiny Hunt.

positional arguments:
  Encounters.txt        The file location for the number of encounters
  OBS_IP                The IP of the OBS instance
  PORT                  The port for the OBS instance

optional arguments:
  -h, --help            show this help message and exit
  --password [PASSWORD]
                        The password for the OBS instance
  --timeout [TIMEOUT]   The time (in seconds) to start the "End Stream" screen
  --EndStreamTime [ENDSTREAMTIME]
                        The time (in seconds) to wait on the end screen
  --EndStreamName ENDSTREAMNAME
                        The name for the end stream scene

```

You must set up OBS in the way described [here](https://github.com/Elektordi/obs-websocket-py).
neroRL 0.0.4
A library for Deep Reinforcement Learning (PPO) in PyTorch
Requires Python: >=3.6
Keywords: Deep Reinforcement Learning,PyTorch,Proximal Policy Optimization,PPO,Recurrent,Recurrence,LSTM,GRU

# neroRL

neroRL is a PyTorch based research framework for Deep Reinforcement Learning specializing on Recurrent Proximal Policy Optimization.
Its focus is set on environments that are procedurally generated, while providing some usefull tools for experimenting and analyzing a trained behavior.

# Features
- Environments:
  - [Obstacle Tower](https://github.com/Unity-Technologies/obstacle-tower-env)
  - [Unity ML-Agents](https://github.com/Unity-Technologies/ml-agents)
  - [Procgen](https://github.com/openai/procgen)
  - [Gym-Minigrid](https://github.com/maximecb/gym-minigrid) (Vector (one-hot) or Visual Observations (84x84x3))
  - [Gym CartPole](https://github.com/openai/gym) using masked velocity
  - [DM Ballet](https://github.com/deepmind/deepmind-research/tree/master/hierarchical_transformer_memory/pycolab_ballet)
- Proximal Policy Optimization (PPO)
  - Discrete and Multi-Discrete Action Spaces
  - Vector and Visual Observation Spaces (either alone or simultaneously)
  - [Recurrent Policies using Truncated Backpropagation Through Time](https://github.com/MarcoMeter/recurrent-ppo-truncated-bptt)
  - Shared and None-Shared Parameters across the Policy and the Value Function
- Decoupled Proximal Policy Optimization
  - Same features as PPO, but the parameters, as well as the gradients, are decoupled
  - Decoupled Advantage Actor-Critic (DAAC, [Raileanu & Fergus, 2021](https://arxiv.org/abs/2102.10330))
    - The actor network estimates the policy and the advantage function

# Obstacle Tower Challenge
Originally, this work started out by achieving the 7th place during the [Obstacle Tower Challenge](https://blogs.unity3d.com/2019/08/07/announcing-the-obstacle-tower-challenge-winners-and-open-source-release/) by using a relatively simple FFCNN. This [video](https://www.youtube.com/watch?v=P2rBDHBHxcM) presents some footage of the approach and the trained behavior:

<p align="center"><a href="http://www.youtube.com/watch?feature=player_embedded&v=P2rBDHBHxcM
" target="_blank"><img src="http://img.youtube.com/vi/P2rBDHBHxcM/0.jpg" 
alt="Rising to the Obstacle Tower Challenge" width="240" height="180" border="10" /></a></p>

Recently we published a [paper](https://arxiv.org/abs/2004.00567) at CoG 2020 (best paper candidate) that analyzes the taken approach. Additionally the model was trained on 3 level designs and was evaluated on the two left out ones. The results can be reproduced using the [obstacle-tower-challenge](https://github.com/MarcoMeter/neroRL/tree/obstacle-tower-challenge) branch.

# Getting Started

To get started check out the [docs](/docs/)!
supervisor-gateway 0.4.0
An RESTful supervisor gateway with paginated and cached process info
Requires Python: >=3.8

<p align="center">
<a href="https://github.com/ppd0705/supervisor-gateway/actions">
    <img src="https://github.com/ppd0705/supervisor-gateway/workflows/Test%20Suite/badge.svg" alt="Test Suite">
</a>
<a href="https://pypi.org/project/supervisor-gateway" target="_blank">
    <img src="https://img.shields.io/pypi/v/supervisor-gateway?color=%2334D058&label=pypi%20package" alt="Package version">
</a>
<a href="https://pypi.org/project/supervisor-gateway" target="_blank">
    <img src="https://img.shields.io/pypi/pyversions/supervisor-gateway.svg?color=%2334D058" alt="Supported Python versions">
</a>
</p>

# supervisor-gateway

A RESTful supervisor gateway with paginated and cached process info

## Features
- RESTful API to `supevisord` support
- A `eventlistener` role for events subscription  
- Cached status API with pagination 

## Architecture

<a href="https://github.com/ppd0705/supervisor-gateway">
    <img src="https://github.com/ppd0705/supervisor-gateway/blob/master/architecture.png" alt="supervisor-gateway">
</a>

## Install

from pypi

```shell
pip install supervisor-gateway
```

or install locally

```shell
make install
```

## Usage

`supervisor-gateway` running as child process of supervisord, there is a [supervisor config example](config/supervisor/supervisor_gateway.conf)

```ini
[eventlistener:supervisor_gateway]
command = python -m supervisor_gateway
events = PROCESS_STATE,SUPERVISOR_STATE_CHANGE,PROCESS_GROUP
environment = SG_LOG_LEVEL="DEBUG",SG_RPC="http://localhost:9011/RPC2",SG_LOG_FILE=
              "supervisor_gateway.log",SG_HOST="localhost",SG_PORT="1234"
stderr_logfile = supervisor_gateway.err.log
stderr_logfile_maxbytes = 10MB
stderr_logfile_backups = 2
buffer_size = 1024
```

some supported environments blow:
- SG_HOST: listen host
- SG_PORT: listen port
- SG_RPC: supervisord rpc url
- SG_LOG_LEVEL: log level
- SG_LOG_FILE: log file

update supervisor conf
```shell
supervisorctl update supervisor_gateway
```

check it 
```shell
curl  http://localhost:1234/rpc/state  
```

interact with api document in the browser [http://localhost:1234/docs](http://localhost:1234/docs)

## TODO
- [x] Add unit test
- [x] Add more API
- [x] Add API documents

## License

Distributed under the MIT License. See [LICENSE](LICENSE) for more information.

## Acknowledgments

- [supervisor](https://github.com/Supervisor/supervisor)
- [multivisor](https://github.com/tiagocoutinho/multivisor)
- [aioconsole](https://github.com/vxgmichel/aioconsole)
- [aiohttp-xmlrpc](https://github.com/mosquito/aiohttp-xmlrpc)
- [fastapi](https://github.com/tiangolo/fastapi)