Reinforcement learning (RL) isn't covered much in Julia packages. There is a collection of RL algorithms over MDP in package: https://github.com/cpritcha/MDP. There is a collection of IJulia notebooks from a Stanford course that cover more RL algorithms: https://github.com/sisl/aa228-notebook/tree/master
Unfortunately, more advanced function approximation techniques, beyond look-up table, that allow to tackle large action-state spaces, are nowhere to find. Couple a month ago, Shane Conway, the guy behind RL-Glue <http://glue.rl-community.org/wiki/Main_Page>, talked about developing Julia RL-Glue client. If that happens, it would be quite simple to use various advanced RL algorithms, including value function approximators, in Julia. On Saturday, November 22, 2014 11:12:29 PM UTC-5, Pileas wrote: > > Some problems have the so-called curse of dimensionality and curse of > modeling. For this reason Bersekas and Tsimtsiklis (at MIT) introduced the > so-called Neuro-Dynamic Programing. > > Does Julia offer support for the aforementioned and if not, how about the > future? >
