Feedforward Closedloop Learning (FCL)
Project description
Feedforward closed loop learning (FCL) is a learning algorithm which adds flexibility to autonomous agents.
A designer defines an initial behaviour as a reflex and then FCL learns from the reflex to develop new flexible behaviours.
The Python documentation can be obtained with:
import feedforward_closedloop_learning as fcl help(fcl)
The Python API is identical to the C++ API: The header files fcl.h, neuron.h and layer.h contain docstrings for all important calls. The doxygen generated documentation can be found here: https://github.com/glasgowneuro/feedforward_closedloop_learning/tree/master/docs
The best way to get started is to look at the script in tests_py: https://github.com/glasgowneuro/feedforward_closedloop_learning/tree/master/tests_py
A full application using the Python API is our vizdoom agent: https://github.com/glasgowneuro/fcl_demos
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for feedforward_closedloop_learning-1.2.5.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | cdbb6989f5f7f808df15959177f2577be15bc593049d3efaf0af8a4ac0446fd0 |
|
MD5 | a3294db9de8341dba575718a7f01e89c |
|
BLAKE2b-256 | 4c92857b93e1fe9237261c5302dea4e7b1fb9f4f2b028df081dfebce26cdbeae |
Hashes for feedforward_closedloop_learning-1.2.5-py3.6-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 53650f0317e432b297d7749725c879b231aaed669909929280cc264f39bcb0b0 |
|
MD5 | eced0ffda3d5cbd3fce41a0652d83936 |
|
BLAKE2b-256 | ba799a1d05e3f0436b7803067392b4306bf0ff9023eb1cadee90a4675e808ae3 |
Hashes for feedforward_closedloop_learning-1.2.5-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8bf11ad6c29de131e6a82197d47695c87167c3a3991cbd2ec717e874d5993c4a |
|
MD5 | 41e1cc443cf6ee47787bd0fa99729993 |
|
BLAKE2b-256 | cdccdd6d4c19d1ac9239d889ebf704880a37a070efd1e95060d63510dbbf9eda |