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s-shiroshita authoreds-shiroshita authored
NeurIPS 2020: MineRL Competition SQIL Baseline with PFRL
This repository is a SQIL baseline submission example with PFRL, based on the MineRL Rainbow Baseline with PFRL.
For detailed & latest documentation about the competition/template, see the original template repository.
This repository is a sample of the "Round 1" submission, i.e., the agents are trained locally.
test.py is the entrypoint script for Round 1.
Please ignore train.py, which will be used in Round 2.
train/ directory contains baseline agent's model weight files trained on MineRLObtainDiamondDenseVectorObf-v0.
List of current baselines
- Rainbow
- SQIL <-- We are here
How to Submit
After signing up the competition, specify your account data in aicrowd.json.
See the official doc
for detailed information.
Then you can create a submission by making a tag push to your repository on https://gitlab.aicrowd.com/. Any tag push (where the tag name begins with "submission-") to your repository is considered as a submission.
If everything works out correctly, you should be able to see your score on the competition leaderboard.
About Baseline Algorithm
This baseline consists of three main steps:
- Apply K-means clustering for the action space with the demonstration dataset.
- Calculate cumulative reward boundaries for each subtask so that the amount of frames in the demonstration is equally separated.
- Apply SQIL algorithm on the discretized action space.
K-means in the step 1 is from scikit-learn.
In this baseline, the agent maintains two clusters with different sampling criteria: One is sampled from frames changing vector in the next observation and the other is from the remaining.
The implementation of SQIL is not included into PFRL agents but it is based on PFRL's DQN implementation.