semvec/pypi-packages huggingface dataset:
mlpro-int-pettingzoo 1.0.2
MLPro: Integration PettingZoo
Requires Python: >=3.11

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<!---
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<img src="https://github.com/fhswf/MLPro-Int-PettingZoo/blob/main/doc/logo/original/logo.png?raw=True" align="right" width="40%"/>

# MLPro-Int-PettingZoo - Integration between PettingZoo and MLPro
Welcome to MLPro-Int-PettingZoo, an extension to MLPro to integrate the PettingZoo package. MLPro is a middleware framework for standardized machine learning in Python. It is developed by the South Westphalia University of Applied Sciences, Germany, and provides standards, templates, and processes for hybrid machine learning applications. PettingZoo, in turn, provides a diverse suite of environments for multi-agent reinforcement learning.

MLPro-Int-PettingZoo offers wrapper classes that allow the reuse of environments from PettingZoo in MLPro, or the other way around.

### Learn more
[MLPro - The integrative middleware framework for standardized machine learning in Python](https://mlpro.readthedocs.io)   
[MLPro-Int-PettingZoo - Integration of PettingZoo into MLPro](https://mlpro-int-pettingzoo.readthedocs.io)   
[PettingZoo - An API standard for multi-agent reinforcement learning](https://pettingzoo.farama.org/index.html)
connect-four-ai 1.0.0
A high performance implementation of a perfect Connect Four solver, written in Rust.
Requires Python: >=3.13

# Connect Four AI

[![Crates.io Version](https://img.shields.io/crates/v/connect-four-ai)](https://crates.io/crates/connect-four-ai)
[![PyPI Version](https://img.shields.io/pypi/v/connect-four-ai)](https://pypi.org/project/connect-four-ai)
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[![docs.rs](https://img.shields.io/docsrs/connect-four-ai)](https://docs.rs/connect-four-ai)

A high-performance, perfect Connect Four solver written in Rust, with bindings for Python and WebAssembly.

![Connect Four GIF](https://github.com/user-attachments/assets/bb7dff1f-3a27-4f0a-b6ab-b46f19df6fd6)

This library can strongly solve any Connect Four position and determine the optimal move.
For full details, performance benchmarks, and demos, please see the main
[GitHub Repository](https://github.com/benjaminrall/connect-four-ai).

## Key Features

- **Perfect Solver**: Implements an optimised negamax search,
  which utilises alpha-beta pruning and a transposition table
  to quickly converge on exact game outcomes.

- **AI Player**: Features an AI player with configurable difficulty. It can play
  perfectly by always choosing the optimal move, or can simulate a range of
  skill levels by probabilistically selecting moves based on their scores.

- **Bitboard Representation**: Uses a compact and efficient bitboard representation for
  game positions, allowing for fast move generation and evaluation.

- **Embedded Opening Book**: Includes a pre-generated opening book of depth 8, which is
  embedded directly into the binary for instant lookups of early-game solutions.

- **Parallel Book Generator**: A tool built with `rayon` for generating new, deeper
  opening books.

- **Cross-Platform**: Available as a Rust crate, Python package, and WebAssembly module for
  seamless integration into a wide range of projects.

## Installation

To use the library, you can simply install it using `pip`:

```shell
pip install connect-four-ai
```

### Example Usage

This is a basic example of how to use the `Solver` to find the score of a position:

```python
from connect_four_ai import Solver, Position

# Creates a position from a sequence of 1-indexed moves
position = Position.from_moves("76461241141")

# Initialises and uses the Solver to calculate the exact score of the position
solver = Solver()
score = solver.solve(position)

print(score)    # Output: -1
```

## License

This project is licensed under the **MIT License**. See the [`LICENSE`](https://github.com/benjaminrall/connect-four-ai/blob/main/LICENSE) file for details.
mlpro-int-gymnasium 1.0.4
MLPro: Integration Gymnasium
Requires Python: >=3.11

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<!---
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--->
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<img src="https://github.com/fhswf/MLPro-Int-Gymnasium/blob/main/doc/logo/original/logo.png?raw=True" align="right" width="40%"/>

# MLPro-Int-Gymnasium - Integration between Gymnasium and MLPro
Welcome to MLPro-Int-Gymnasium, an extension to MLPro to integrate the Gymnasium package. MLPro is a middleware framework for standardized machine learning in Python. It is developed by the South Westphalia University of Applied Sciences, Germany, and provides standards, templates, and processes for hybrid machine learning applications. Gymnasium, in turn, provides a diverse suite of reinforcement learning environments.

MLPro-Int-Gymnasium offers wrapper classes that allow the reuse of environments from Gymnasium in MLPro, or the other way around.

### Learn more
[MLPro - The integrative middleware framework for standardized machine learning in Python](https://mlpro.readthedocs.io)   
[MLPro-Int-Gymnasium - Integration of Gymnasium into MLPro](https://mlpro-int-gymnasium.readthedocs.io)   
[Gymnasium - An API standard for reinforcement learning with a diverse collection of reference environments](https://gymnasium.farama.org/index.html)