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Computer Science > Machine Learning

arXiv:2209.00588 (cs)
[Submitted on 1 Sep 2022]

Title:Transformers are Sample Efficient World Models

Authors:Vincent Micheli, Eloi Alonso, Francois Fleuret
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    Abstract: Deep reinforcement learning agents are notoriously
    sample inefficient, which considerably limits their application
    to real-world problems. Recently, many model-based methods have
    been designed to address this issue, with learning in the
    imagination of a world model being one of the most prominent
    approaches. However, while virtually unlimited interaction with a
    simulated environment sounds appealing, the world model has to be
    accurate over extended periods of time. Motivated by the success
    of Transformers in sequence modeling tasks, we introduce IRIS, a
    data-efficient agent that learns in a world model composed of a
    discrete autoencoder and an autoregressive Transformer. With the
    equivalent of only two hours of gameplay in the Atari 100k
    benchmark, IRIS achieves a mean human normalized score of 1.046,
    and outperforms humans on 10 out of 26 games. Our approach sets a
    new state of the art for methods without lookahead search, and
    even surpasses MuZero. To foster future research on Transformers
    and world models for sample-efficient reinforcement learning, we
    release our codebase at this https URL.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
          Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2209.00588 [cs.LG]
          (or arXiv:2209.00588v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2209.00588
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Vincent Micheli [view email]
[v1] Thu, 1 Sep 2022 17:03:07 UTC (290 KB)
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