DLR-RM/stable-baselines3

Assert that the VecNormalize wrapper handles the new truncations correctly

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#1,093 opened on Oct 4, 2022

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 (2 comments) (0 reactions) (0 assignees)Python (6,550 stars) (1,407 forks)batch import
help wanted

Description

I need to think more about that one, but my guess is that it should not matter much, the scaling is meant to avoid large values and to evolve with the agent performance. Still, we should double check that. Could you open an issue so we don't forget?

Originally posted by @araffin in https://github.com/DLR-RM/stable-baselines3/pull/780#discussion_r985247261

Contributor guide

Tech stack
pythonpytorch
Domain
machine learningai
Issue type
research
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
familiarity with VecNormalize wrapperunderstanding of RL truncation vs termination
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
30
Research direction
Investigate the VecNormalize implementation in stable baselines3 to ensure that truncation steps (as opposed to terminal steps) are correctly handled. Review the discussion in PR #780, particularly the comment by @araffin, and examine the relevant source code in vec normalize.py. Look for any potential issues with normalization updates during non terminal transitions and verify that the scaling logic accommodates truncations without bias.