What we do
Ohara University publishes long-form, technically rigorous explanations of reinforcement learning methods — every major algorithm family, covered from its earliest and simplest form through its modern state-of-the-art variants, with working code, comparison tables, and real-world applications. The site is built to function as a complete RL curriculum, not a collection of unrelated posts.
Editorial standards
We hold every article to the same bar before it publishes:
- Technical claims are traceable to the original papers that introduced them (see the References section on each article).
- Code examples illustrate the actual mechanism of an algorithm, not a simplified analogy that would mislead a reader trying to implement it.
- Every article explicitly states what it's leaving out or where a technique's limitations are, rather than only presenting its strengths.
- Content is organized by algorithm family and cross-linked, so a reader can trace how one method builds on or departs from another.
Who's behind this
Ohara University is an independent publication maintained by a single editor with a background in backend and ML systems engineering. We publish under the Ohara University name rather than an individual byline, and we stand behind the accuracy of everything published here — if you find an error, we want to know about it (see Contact).
Corrections
If you spot a technical inaccuracy, a broken citation, or an outdated claim in any article, please reach out via the contact page. We review and correct errors promptly and note significant corrections with an updated revision date on the article itself.