Skip to main content

A GUI tool for building Pygame applications and Reinforcement Learning

Project description

Reinforcement Learning with Pygame and Python

This project was inspired by the development of technologies in AI and reinforcement learning applications.
Python has seen the development of very powerful pipelines and libraries like Pytorch and Tensorflow that allow users to create complex projects with deep learning, and therefore gives Python an advantage over perhaps faster but harder to integrate compiled programming languages like C, C# or C++.
However, while Python has the AI libraries, it doesn't have a formal GUI-based library for game-like development like Unity or UnrealEngine does, and while emulating the complexity of building an engine doesn't overcome the benefits of library integration at this point, Python does offer tools for the creation of windows and the coding of agents with the library Pygame.
We hope to narrow the gap between user friendly environment design by providing a simple GUI that works on pyQt5, and offer additional tools to expedite and, to some extent, standardize the structure of Reinforcement Learning Agents that can be deployed in Pygame environments.

This library supplements the online book "The RL Playground with Python", which we consider a good resource for information on the deployment of reinforcement learning agents at an introductory level.
https://ugarcil.github.io/The_RL_Playground_with_python/

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rlpp-0.2.3.tar.gz (11.5 kB view details)

Uploaded Source

File details

Details for the file rlpp-0.2.3.tar.gz.

File metadata

  • Download URL: rlpp-0.2.3.tar.gz
  • Upload date:
  • Size: 11.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.4

File hashes

Hashes for rlpp-0.2.3.tar.gz
Algorithm Hash digest
SHA256 333ec5cefe1ef0a6e032de18edfda50458317ca7c0723d88447e7290962ac540
MD5 cf00b1d23a19bd7f6d2beaa399d1963c
BLAKE2b-256 cafe90d3446ca56e90a7903dd131af3a0224402a6b8a59393d1bb47e47b27720

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page