Smaller World Models for Reinforcement Learning
This work addresses sample efficiency for reinforcement learning practitioners, but it is incremental as it builds on existing setups and achieves similar performance with a smaller model.
The paper tackles sample efficiency in reinforcement learning by proposing a new neural network architecture for world models using a VQ-VAE and convolutional LSTM, achieving comparable performance to SimPLe on 36 Atari environments with only 100K real interactions while using a significantly smaller model.
Sample efficiency remains a fundamental issue of reinforcement learning. Model-based algorithms try to make better use of data by simulating the environment with a model. We propose a new neural network architecture for world models based on a vector quantized-variational autoencoder (VQ-VAE) to encode observations and a convolutional LSTM to predict the next embedding indices. A model-free PPO agent is trained purely on simulated experience from the world model. We adopt the setup introduced by Kaiser et al. (2020), which only allows 100K interactions with the real environment. We apply our method on 36 Atari environments and show that we reach comparable performance to their SimPLe algorithm, while our model is significantly smaller.