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Dissecting Reinforcement Learning

A public MIT-licensed repository of Python examples, PDFs and reference links accompanying an eight-part series on reinforcement learning. It includes standalone environments for practice, rather than a packaged library or documented API.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Dissecting Reinforcement Learning — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want educational material and small Python examples to accompany reinforcement-learning study, or standalone environments for trying basic experiments.

Who it is for

  • Learners following reinforcement-learning concepts alongside code examples.
  • Practitioners seeking small examples and standalone environments for exploration.

Documented uses

  • Study reinforcement-learning topics using the series, PDFs and linked references.
  • Explore methods such as Q-learning, actor-critic and multi-armed bandits through example code.
  • Try control tasks in standalone environments such as mountain car and inverted pendulum.
Before you choose

Requirements & limits

The repository is public and open source under the MIT license.

  • NumPy is the stated code requirement; some examples need Matplotlib for visualization or animation.
  • The examples are organized as educational source code, not a packaged library or documented API.
  • The repository describes use on Linux, Windows and OS X, and also mentions Raspberry Pi, BeagleBone and Intel Edison.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Use a standalone environment in a Python script

Run a short episode in the inverted-pendulum environment and save the episode as a GIF.

Before you start

  • Python and NumPy; the repository says no special installation procedure is required.
  • Matplotlib for environment rendering and animation.
  • The inverted-pendulum environment file copied into the same folder as the Python script.

Steps

  1. Copy the inverted-pendulum environment file from the repository’s environments folder into the project folder.

  2. In a Python script, import InvertedPendulum and create an instance with the documented parameters, such as pole_mass=2.0, cart_mass=8.0, pole_lenght=0.5, and delta_t=0.1.

  3. Call reset(exploring_starts=True), then repeatedly call step(action) for up to 100 steps, stopping early when done is true; the documented example chooses actions 0, 1, or 2 randomly.

  4. Call render(file_path='./inverted_pendulum.gif', mode='gif') to save the last episode.

Expected outcome

A short inverted-pendulum episode is simulated, with the last episode saved as a GIF.

Guide limitations

  • The example uses random actions rather than a trained policy.
  • An episode can end early if the pole falls; the example’s 100 steps at delta_t=0.1 correspond to 10 seconds.
  • Rendering the GIF requires Matplotlib.
  • The README example spells the constructor parameter pole_lenght; follow the environment’s documented code for the exact parameter name.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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17 October 2026
Original website title
mpatacchiola/dissecting-reinforcement-learning · Official homepage title · retained in its source language
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