[R] Best practices for implementing and benchmarking a custom PyTorch RL algorithm?

Reddit r/MachineLearning / 4/7/2026

💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage

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Key Points

The post asks for best practices and learning resources on implementing custom reinforcement learning algorithms in PyTorch, particularly after the theoretical work is done.
It explores how much effort to invest in engineering hygiene such as code cleanliness, directory structure, and organization before benchmarking.
It asks about standard benchmark environments for Gym-based evaluation and whether there are recommended targets for fair comparison.
It considers reproducibility and portability issues, including whether to Dockerize the setup and ensuring the code runs consistently across macOS and Linux systems.

Hey, I'm working on a reinforcement learning algorithm. The theory is complete, and now I want to test it on some Gym benchmarks and compare it against a few other known algorithms. To that end, I have a few questions:

Is there a good resource for learning how to build custom PyTorch algorithms?
How optimized or clean does my code need to be? Should I spend time cleaning things up, creating proper directory structures, etc.?
Is there a known target environment or standard? Do I need to dockerize my code? I'll likely be writing it on a Mac system. Do I also need to ensure it works on Linux?

submitted by /u/ANI_phy
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