RLBattery

Datasets
Literature
Function Approximation
Sutton & Barto
Quick QLearning Tutorial - https://studywolf.wordpress.com/2012/11/25/reinforcement-learning-q-learning-and-exploration/
- Check this for the implementation and data structures
- Read relevant chapter in Sutton & Barto: https://webdocs.cs.ualberta.ca/~sutton/book/ebook/node65.html
Deep QLearning with Neural Nets for Function Approx - https://www.nervanasys.com/demystifying-deep-reinforcement-learning/
- Don't fret, this is very simple and has well asbtracted ideas. Mainly focuses on function approximation.
- Read all the sections, they've been explained as black boxes.
- Experience Replay: Online training in minibatches.

Name		Name	Last commit message	Last commit date
Latest commit History 34 Commits
README.md		README.md
agent__e_cap6.pkl		agent__e_cap6.pkl
agent__e_cap69.pkl		agent__e_cap69.pkl
agent__e_cap6912.pkl		agent__e_cap6912.pkl
agent__e_cap691215.pkl		agent__e_cap691215.pkl
agent__summer.pkl		agent__summer.pkl
agent__winter.pkl		agent__winter.pkl
agrregate_solar.csv		agrregate_solar.csv
environment.py		environment.py
function_approximation.py		function_approximation.py
learningAgent.py		learningAgent.py
load_data.csv		load_data.csv
load_data_peak6.csv		load_data_peak6.csv
main.py		main.py
main_pseudo.py		main_pseudo.py
model_store_k1_winter		model_store_k1_winter
refactored_tabular.ipynb		refactored_tabular.ipynb
solardata.csv		solardata.csv
test_main.py		test_main.py

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