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Raw results #11
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Yes, I can release the raw results and I would try to do so by the end of this week. Which results do you specifically need? |
Thank you! We are looking to compare with offline REM and offline QR DQN on 1%, 10%, 20% and 100% of data for the following games: breakout, seaquest, pong, asterix, and qbert. |
To clarify:
These particular comparisons are becoming popular following the QR-DQN paper. |
I think you meant the CQL paper? Actually, I can send you these results directly over email now (as I have them stored as zipped panda dataframes) -- can you please write an email to [email protected] ? |
Resolved offline |
Dear @agarwl, will you provide the raw results? It would be great if you can provide since retaining REM takes many days and computation power. |
@GoingMyWay Yes, I'll post the raw results on github by next month. In the meantime, you can send me an email and I can send you some of those results (for the setting requested above) |
I forgot about this but here are the raw results (as someone requested them again recently). This might be useful for people stumbling upon this in the future. QR-DQN (10% data)Asterix 1293.8620483398402 REM (10% data)Asterix 3912.2522460937203 QR-DQN (1% data)Asterix 359.78555908202 REM (1% data)Asterix 363.27997436524 The file below also contains the raw scores for 20% data (2M corresponds to 1%, 20M to 10% and 40M to 20%). |
To facilitate comparison with a method we are developing, is it possible to release raw results (e.g. similar to dopamine json files?)
These data already "exist" as part of your figures in the appendix of your paper, so what we really want is to produce similar figures (comparing our method with your method) without having to rerun yours from scratch.
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