Sounds like a cool project. Are the state space representations that RL-Glue uses easy to work with?
— John On Nov 24, 2014, at 10:09 PM, [email protected] wrote: > 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, 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?
