[Question] Training reproducibility

Important Note: We do not do technical support, nor consulting and don’t answer personal questions per email. Please post your question on the RL Discord, Reddit or Stack Overflow in that case.

Question

Hey, I am working on an algorithm comparison for my custom environment. The environment generates a random scenario for every rollout. Therefore, training the algorithms based on the same data is preferred.

I have tested with set_random_seed(seed = 1) or env.seed(seed = 1) for the environment. And model = A2C("MlpPolicy", env, seed = 1) for the model.

None of them yields two identical training results.

It is appreciated if you can show me how to set it properly.

Checklist

• I have read the documentation (required)
• I have checked that there is no similar issue in the repo (required)

[qgallouedec]

I advise you to take inspiration from the implementation of the numerous environments integrated with gym 0.21: https://github.com/openai/gym/tree/c755d5c35a25ab118746e2ba885894ff66fb8c43

If you have difficulty doing this, I urge you to ask for help on this discord

[qgallouedec]

Sb3 works with gym 0.21 (for now). The reset method in gym 0.21 doesn’t take any argument.

In gym 0.22 and above, reset takes several args including the seed. You are probably referring to the doc of gym 0.24. Aren’t you?