Practical overview
When it fits
Use it when you want code examples to study or adapt, especially when moving from basic Grid World algorithms to deep-learning examples for CartPole or Atari.
Who it is for
- Learners comparing reinforcement-learning algorithms through code examples.
- Developers building tutorials or adapting baseline examples for reinforcement-learning experiments.
Documented uses
- Inspect how different algorithms are represented in Python.
- Run documented Grid World or CartPole examples.
- Extend examples with additional methods, visualizations or benchmark environments.
Requirements & limits
The GitHub repository is public and identifies an MIT license. Setup instructions require Python 3.11 and uv.
- Examples are organized by environment and provided as separate algorithm files.
- The documented setup uses Python 3.11 and uv.
- Atari Weights & Biases logging is optional and requires the user's own account and login.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the repository
Install the documented project dependencies before choosing an example to run.
Before you start
- Python 3.11
- uv
Steps
Clone the repository.
Change into the reinforcement-learning directory.
Run the command below.
uv sync
Expected outcome
The repository dependencies are synchronized with uv, preparing the project for its documented run commands.
Guide limitations
- This guide covers setup only; it does not verify successful installation or execution.
Run a Grid World SARSA example
Launch the repository's documented Grid World SARSA script.
Before you start
- Python 3.11
- uv
- The repository cloned and dependencies synchronized.
Steps
From the repository root, change into
1-grid-world.Run the command below.
uv run python 3-sarsa.py
Expected outcome
The documented SARSA example is invoked through uv.
Guide limitations
- The README provides the invocation but does not specify a guaranteed runtime or result.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 10 October 2026
- Original website title
- rlcode/reinforcement-learning · Official homepage title · retained in its source language