  A number of problems in the processing of sound and natural language, as well
as in other areas, can be reduced to simultaneously reading an input sequence
and writing an output sequence of generally different length. There are well
developed methods that produce the output sequence based on the entirely known
input. However, efficient methods that enable such transformations on-line do
not exist. In this paper we introduce an architecture that learns with
reinforcement to make decisions about whether to read a token or write another
token. This architecture is able to transform potentially infinite sequences
on-line. In an experimental study we compare it with state-of-the-art methods
for neural machine translation. While it produces slightly worse translations
than Transformer, it outperforms the autoencoder with attention, even though
our architecture translates texts on-line thereby solving a more difficult
problem than both reference methods.
