Fractal AI goes public!

Today we are glad to announce that we are finally releasing the "Fractal AI" artificial intelligence framework to the public!

This first release includes:

• Fractal AI: A fragile theory of intelligence A nice to read small book in PDF with theory, algorithm, pseudo-code, experiments, etc.
• github.com/FragileTheory/FractalAI with working python code to beat almost all of the actual Atari games.
• Tons of videos showing Fractal AI at work, along with some talks, etc.

We have modified Fractal AI a little so now it is more powerful than ever, in fact we have been able to beat a lot of the actual records (from state-of-the-art neural network like DQN or A3C) with about 1000 samples per strep (and remember, no learning, no adaptation to any game, same code for all of them).

We are specially proud of beating even some absolute human world records, but hey, it was going to happen anyhow!

Fractal AI playing Ms Packman. It reached an unknow score limit of 999,999 present in a total of 15 games. Fractal AI beated the best human records in 49 out of 50 games.



The parameters for testing on Atari games are few and well explained in the hit-and-run notebook example included: Name of the game, and 3 simple numbers to adjust time horizon, reaction time, and number of paths. The CPU needed at any moment auto adjusts -but you can and should hard-limit it- so you can beat some games like boxing in real time.


I copy-paste here the abstract and a very preliminary score table from the great intro Guillem has done for the release:

Abstract

Fractal AI is a theory for general artificial intelligence. It allows to derive 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 this repository 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 [1].
The code provided shows how it is now possible to beat some of the current state of the art benchmarks on Atari games, using less than 1000 samples to calculate each one of the actions when MCTS uses 3 Million samples. Among other things, Fractal AI makes it possible to generate a huge database of top performing examples with 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, while maintaining control over any aspect of the behavior 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.