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,
setting a new state of the art for methods without lookahead search. To foster
future research on Transformers and world models for sample-efficient
reinforcement learning, we release our code and models at
this https URL.

IRIS 是一个深度强化学习代理，学习于由离散自动编码器和自回归 Transformer 组成的世界模型中，仅用两小时的时间即可在 Atari 100k 基准测试中实现 1.046 的平均人类标准化得分，在 26 个游戏中有 10 个游戏超过了人类的表现，是一种有效的世界模型学习方法。