My good friend and close colleague Guillem had a really busy year attending talks about Reinforced Learning in several events like Piter Py 2017 (Saint Petersburg, Russia), Europython 2018 (Edinburgh, UK) or PyConEs 2018 (Málaga, Spain), and PyData Mallorca (among others!) introducing Fractal Monte Carlo to a broad audience.
All the talks versed about RL, but the talks held at Europython (english) and PyConES (spanish) were both about "hacking RL" by introducing Fractal Monte Carlo (FMC) algorithm as a cheap and efficient way to generate lots of high quality rollouts of the game/system being controlled.
Wednesday, 24 October 2018
Tuesday, 16 October 2018
Graph entropy 7: slides
After the series of six posts about Tree-Graph Entropy (starting here), I have prepared a short presentation about Graph Entropy, mainly to clarify the concepts to my own (and to anyone interested) and present some real-world use cases.
One of the most interesting ideas introduced in this presentation is a method for, once you had defined the entropy of all the nodes in a static disrected and acyclic and directed graph (a tree), to easily update all those entropy values as the graph evolves over time, both altering the conditional probability of some connections, as also by adding or taking connections, by considering nodes and connection as cellular automaton that can adjust its internal entropies asynchronously.
You can also jump to the original google slides version if you want to comment on a particular slide.
If this was not enought for you and what to read more weird things about those entropies, you can dive into the unknown realm of negative probabilities entropy here!
Update (24 Oct 2018): this post was referenced in the article "A Brief Review of Generalized Entropies"where the (c, d) exponents of these generalized entropies are calculated.
One of the most interesting ideas introduced in this presentation is a method for, once you had defined the entropy of all the nodes in a static disrected and acyclic and directed graph (a tree), to easily update all those entropy values as the graph evolves over time, both altering the conditional probability of some connections, as also by adding or taking connections, by considering nodes and connection as cellular automaton that can adjust its internal entropies asynchronously.
You can also jump to the original google slides version if you want to comment on a particular slide.
If this was not enought for you and what to read more weird things about those entropies, you can dive into the unknown realm of negative probabilities entropy here!
Update (24 Oct 2018): this post was referenced in the article "A Brief Review of Generalized Entropies"where the (c, d) exponents of these generalized entropies are calculated.
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