# Transformers are Sample Efficient World Models

**Authors:** Vincent Micheli, Eloi Alonso, François Fleuret  
**arXiv:** [2209.00588](https://arxiv.org/abs/2209.00588)  
**Date:** 2022-09-01  
**Summary page:** https://www.summarizepaper.com/fr/paper/2209.00588v1/

## 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 https://github.com/eloialonso/iris.