  Fractal AI is a theory for general artificial intelligence. It allows
deriving new mathematical tools that constitute the foundations for a new kind
of stochastic calculus, by modelling information using cellular automaton-like
structures instead of smooth functions. In the repository included we are
presenting a new Agent, derived from the first principles of the theory, which
is capable of solving Atari games several orders of magnitude more efficiently
than other similar techniques, like Monte Carlo Tree Search. The code provided
shows how it is now possible to beat some of the current State of The Art
benchmarks on Atari games, without previous learning and using less than 1000
samples to calculate each one of the actions when standard MCTS uses 3 Million
samples. Among other things, Fractal AI makes it possible to generate a huge
database of top performing examples with a very little amount of computation
required, transforming Reinforcement Learning into a supervised problem. The
algorithm presented is capable of solving the exploration vs exploitation
dilemma on both the discrete and continuous cases, while maintaining control
over any aspect of the behaviour of the Agent. From a general approach, new
techniques presented here have direct applications to other areas such as
Non-equilibrium thermodynamics, chemistry, quantum physics, economics,
information theory, and non-linear control theory.
