The moment a reinforcement learning algorithm moves from theory to benchmark, the real test begins, and it's not the one you'd expect. The hardest part isn't the math or the code itself; it's building a comparison you can actually trust. For the researcher asking about PyTorch best practices, code cleanliness, and cross-platform compatibility, the answer is refreshingly direct: your credibility depends less on elegance and more on transparency.
Start with the fundamentals. There are solid, well-documented resources for building custom PyTorch algorithms, the official PyTorch reinforcement learning tutorials, the Stable Baselines3 documentation, and open-source repositories like CleanRL. These aren't just learning tools; they're your reference points for what "standard" looks like. If you're unsure whether your implementation matches community expectations, study how these projects structure their code. They exist because the field needed common ground. Use them as your baseline, then deviate only when your algorithm genuinely requires it.
Now, about code quality: yes, clean your code, but not for the reasons you might think. A tidy directory structure and clear variable names aren't about impressing anyone. They're about making your work auditable. When you report results, someone should be able to read your code and understand exactly what you ran. If they can't, your results are effectively unverifiable. That's not a stylistic preference; it's a scientific necessity. Optimized code matters less than readable code. A slower but clearer implementation that someone can follow beats a cleverly optimized one that reads like a puzzle. The Gym environments you're testing on don't care about your loop unrolling; your future self and your reviewers will.
As for the environment question, dockerize, and don't skip Linux. You can develop on a Mac, but if you're benchmarking against known algorithms, you're implicitly claiming comparability. That claim falls apart if your dependencies differ from the standard stack. Docker gives you a reproducible snapshot of your environment, which is the only way to ensure your results aren't an artifact of your machine's quirks. Linux is the de facto standard for RL research, not because it's fashionable, but because that's where the baseline implementations live. If your code only works on macOS, you've created a barrier to verification. Remove it.
Here's the concrete takeaway: treat your benchmark submission as a publication, not a script. That means versioning your dependencies, documenting your hyperparameters, and running your baselines under the same conditions you run your own algorithm. The moment you skip that step, you're not comparing algorithms, you're comparing anecdotes. And the field has enough of those. Your theory is complete; now give it the fair hearing it deserves. That starts with code someone else can run, on a platform everyone else uses, with results that don't require a leap of faith to believe.