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Reinforcement Learning on Atari 2600 Qbert

684,700ScoreNGU
-27,234.6157,594.575342,423.75527,252.925Jun 4, 2018Sep 16, 2019Dec 29, 2020Apr 12, 2022Jul 26, 2023Nov 6, 2024Feb 19, 2026

Evaluation results

MethodLinks
NGUN=12020.02684,700
NGUN=1, Variant=without RND2020.02647,100
NGUN=322020.02465,800
R2D2Loss=Retrace2020.02415,600
R2D22020.02408,800
R2D2Extension=RND2020.02353,500
BYOL-ExplorePure-exploration regime=true, Episodes=10, Seeds=32022.06200,081.13
OPRIDEObservation=pixel-based, Human-in-the-loop=true2026.0213,535.2
Average HumanPure-exploration regime=true2022.0613,455
Human2020.0213,400
RNDPure-exploration regime=true, Episodes=10, Seeds=32022.0612,233.18
IDRLObservation=pixel-based, Human-in-the-loop=true2026.028,294.1
OPRLObservation=pixel-based, Human-in-the-loop=true2026.027,924.6
PTObservation=pixel-based, Human-in-the-loop=true2026.027,482.9
PT+PDSObservation=pixel-based, Human-in-the-loop=true2026.026,844.2
NSRA-ESinput type=Atari RAM, # of neurons=~6502018.061,350
IDVQ+DRSC+XNESinput type=raw pixel, max run length=200 interactions, frameskip=5, # of neurons=62018.061,250
ICMPure-exploration regime=true, Episodes=10, Seeds=32022.061,062.49
HyperNeatinput type=raw pixel, # of neurons=~30342018.06695
OpenAI ESinput type=raw pixel, # of neurons=~6502018.06147.5