  Recently, model-based reinforcement learning algorithms have demonstrated
remarkable efficacy in visual input environments. These approaches begin by
constructing a parameterized simulation world model of the real environment
through self-supervised learning. By leveraging the imagination of the world
model, the agent's policy is enhanced without the constraints of sampling from
the real environment. The performance of these algorithms heavily relies on the
sequence modeling and generation capabilities of the world model. However,
constructing a perfectly accurate model of a complex unknown environment is
nearly impossible. Discrepancies between the model and reality may cause the
agent to pursue virtual goals, resulting in subpar performance in the real
environment. Introducing random noise into model-based reinforcement learning
has been proven beneficial. In this work, we introduce Stochastic
Transformer-based wORld Model (STORM), an efficient world model architecture
that combines the strong sequence modeling and generation capabilities of
Transformers with the stochastic nature of variational autoencoders. STORM
achieves a mean human performance of $126.7\%$ on the Atari $100$k benchmark,
setting a new record among state-of-the-art methods that do not employ
lookahead search techniques. Moreover, training an agent with $1.85$ hours of
real-time interaction experience on a single NVIDIA GeForce RTX 3090 graphics
card requires only $4.3$ hours, showcasing improved efficiency compared to
previous methodologies.
