Remove reward_normaliztion option in offpolicy algorithm #298
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Codecov Report
@@ Coverage Diff @@
## dev #298 +/- ##
==========================================
+ Coverage 93.89% 93.91% +0.01%
==========================================
Files 47 47
Lines 3241 3235 -6
==========================================
- Hits 3043 3038 -5
+ Misses 198 197 -1
Flags with carried forward coverage won't be shown. Click here to find out more.
Continue to review full report at Codecov.
|
Trinkle23897
approved these changes
Feb 27, 2021
BFAnas
pushed a commit
to BFAnas/tianshou
that referenced
this pull request
May 5, 2024
* remove rew_norm in nstep implementation * improve test * remove runnable/ * various doc fix Co-authored-by: n+e <trinkle23897@gmail.com>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This pr will remove 'reward_normaliztion' option in offpolicy algorithm and related tests/examples because original reward_normaliztion in n_step_return will cause unstability in training when buffer is full (1M steps in the graph below).
