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Raw results #11

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n17s opened this issue Dec 14, 2020 · 8 comments
Open

Raw results #11

n17s opened this issue Dec 14, 2020 · 8 comments
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enhancement New feature or request

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@n17s
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n17s commented Dec 14, 2020

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.

@agarwl
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agarwl commented Dec 16, 2020

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?

@n17s
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n17s commented Dec 16, 2020

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.

@pmineiro
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pmineiro commented Dec 16, 2020

To clarify:

  • the 20% data condition means using only the first 10 million episodes from the online run.
  • the 1% and 10% data conditions are trained on uniform subsamples of the full online run.

These particular comparisons are becoming popular following the QR-DQN paper.

@agarwl
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agarwl commented Dec 16, 2020

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] ?

@n17s
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n17s commented Dec 17, 2020

Resolved offline

@n17s n17s closed this as completed Dec 17, 2020
@GoingMyWay
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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?

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.

@agarwl
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agarwl commented Jan 18, 2021

@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)

@agarwl agarwl reopened this Jan 18, 2021
@agarwl agarwl added the enhancement New feature or request label Mar 15, 2021
@agarwl agarwl pinned this issue May 13, 2021
@agarwl
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agarwl commented Mar 19, 2023

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
Breakout 61.84913024902001
Pong 12.650765800479999
Qbert 9420.50625
Seaquest 353.07070770264

REM (10% data)

Asterix 3912.2522460937203
Breakout 56.91960
Pong 9.52690958976
Qbert 5799.877001953099
Seaquest 3643.4553710937603

QR-DQN (1% data)

Asterix 359.78555908202
Breakout 6.8403110503999995
Pong -14.56116828918
Qbert 155.90773391724002
Seaquest 250.[120996093

REM (1% data)

Asterix 363.27997436524
Breakout 4.46266336442
Pong -20.81458015444
Qbert 160.09661407469997
Seaquest 370.47359313964

The file below also contains the raw scores for 20% data (2M corresponds to 1%, 20M to 10% and 40M to 20%).
uniform.zip

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