I started to write a julia package for reinforcement learning:
Currently it contains a few algorithms for tabular environments and, on a separate branch, linear function approximation for a few TD algorithms. It is easy to use it with POMDPModels.jl of the POMDPs.jl universe, see examples. Whenever I have time I will continue to add support for linear function approximation, which is straightforward, and try to extend it with non-linear function approximation, e.g. using Knet.jl.
Any feedback is highly appreciated.
Please let me know, if you want to collaborate.