  Like a scientist or a playing child, PowerPlay not only learns new skills to
solve given problems, but also invents new interesting problems by itself. By
design, it continually comes up with the fastest to find, initially novel, but
eventually solvable tasks. It also continually simplifies or compresses or
speeds up solutions to previous tasks. Here we describe first experiments with
PowerPlay. A self-delimiting recurrent neural network SLIM RNN is used as a
general computational problem solving architecture. Its connection weights can
encode arbitrary, self-delimiting, halting or non-halting programs affecting
both environment (through effectors) and internal states encoding abstractions
of event sequences. Our PowerPlay-driven SLIM RNN learns to become an
increasingly general solver of self-invented problems, continually adding new
problem solving procedures to its growing skill repertoire. Extending a recent
conference paper, we identify interesting, emerging, developmental stages of
our open-ended system. We also show how it automatically self-modularizes,
frequently re-using code for previously invented skills, always trying to
invent novel tasks that can be quickly validated because they do not require
too many weight changes affecting too many previous tasks.
