PyTorch RL Benchmarking Best Practices

This is a reproducibility question disguised as an implementation question: you do not need a giant framework, but you do need enough structure that results are trustworthy and repeatable.

• –Start with a minimal reference implementation, not a heavyweight architecture; PyTorch tutorials and CleanRL-style single-file baselines are the right level of complexity early on.
• –Benchmark on standard Gymnasium env versions and compare against established baselines with multiple seeds, normalized evaluation, and clear reporting of mean and variance.
• –Keep the code clean enough for experiment hygiene: configs, logging, checkpointing, and deterministic seeds matter more than a perfect directory tree.
• –Docker is optional for prototyping, but it becomes useful when you want exact environment capture, CI, or to avoid dependency drift across machines.
• –Developing on macOS is fine, but for final benchmark runs you should verify on Linux if you want results that map cleanly onto the rest of the RL ecosystem.