An implementation of Locally Weighted Projection Regression
Readme for submission of Imitation Learning Seminar:
Included files:
- HS-IL-Jupyter-Notebook-Peter_Muschick (Folder containing jupyter notebook)
- _README.txt (current file)
- full_dataset_120k.csv (Dataset containing 120k lines of training data, created by harsha_evolution.py)
- harsha_evolution.py (Evolutionary algorithm used to create training data)
- harsha_evolution_cropped.csv (Dataset containing 4,8k lines of training data, created by harsha_evolution.py, only first 100 timestop used)
- HS-IL-Presentation-Peter_Muschick.pdf (file containing the presentation)
- linear.csv (test file containing points in linear order)
- linear_plateau.csv (test file containing points in linear order with a short pleateau)
- lwpr_algorithm.py (lwpr algorithm file)
- main.py (The main python script containing the cartpole open gym environment)
- networking.py (contains the UDP networking part for main.py)
- sharvar_keras.py (generates training data with an Proximal Policy Optimization algorithm)
- sharvar_keras_data.csv (created data from sharvar_keras.py)
- sinus_noise.csv (creates scattered sinus points)
Remarks/explanation to specific files:
main.py. - This file needs to be executed with python 3.x (open gym is only running with python 3.x)
lwpr_algorithm.py - https://github.com/jdlangs/lwpr - Check their README.txt - Basically a python 2.7 interpreter (32 bit) with a few packages (described in their README.txt) and GCC installed on top of it needs to run it