Title: Token Turing Machines. Abstract: We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential decision making. Our model is inspired by the seminal Neural Turing Machine, and has an external memory consisting of a set of tokens which summarise the previous history. This memory is efficiently addressed, read and written using a Transformer as the processing unit/controller at each step. The model's memory module ensures that a new observation will only be processed with the contents of the memory (and not the entire history), meaning that it can efficiently process long sequences with a bounded computational cost at each step. We show that TTM outperforms other alternatives, such as other Transformer models designed for long sequences and recurrent neural networks, on two real-world sequential decision making tasks: online temporal activity localization from videos and vision-based robot action policy learning.