Implement ContinuousBCQPolicy and offline_bcq example #480
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
|
Could you please add |
Codecov Report
@@ Coverage Diff @@
## master #480 +/- ##
==========================================
+ Coverage 94.06% 94.24% +0.17%
==========================================
Files 60 61 +1
Lines 3910 4031 +121
==========================================
+ Hits 3678 3799 +121
Misses 232 232
Flags with carried forward coverage won't be shown. Click here to find out more.
Continue to review full report at Codecov.
|
Trinkle23897
previously approved these changes
Nov 22, 2021
Trinkle23897
approved these changes
Nov 22, 2021
BFAnas
pushed a commit
to BFAnas/tianshou
that referenced
this pull request
May 5, 2024
This PR implements BCQPolicy, which could be used to train an offline agent in the environment of continuous action space. An experimental result 'halfcheetah-expert-v1' is provided, which is a d4rl environment (for Offline Reinforcement Learning). Example usage is in the examples/offline/offline_bcq.py.
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Outdated suggestions cannot be applied.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.
make format(required)make commit-checks(required)This PR implements ContinuousBCQPolicy, which could be used to train offline agent in the environment of continuous action space. Here is an experimental result in 'halfcheetah-expert-v1', a d4rl environment (for Offline Reinforcement Learning).
Example usage is in the
examples/offline/offline_bcq.py.