Author(s)/contributers:
    Ting-Chun Kuo
Date:
    04/26/2022
 <br>
These set of codes demonstrate training TD algorithms in office buildings. The environment is GYM-like in the folder env. The weather data is in the data folder. utils.py includes all the supplimental functions for plotting and etc.
The code includes 4 parts, SARSA.py, Q_learning.py, baseline.py, and validation.py. <br>
In SARSA.py and Q_learning.py, the Q-table is trained and output to two seperate csv files:
Q_SARSA.csv and Q_lr.csv <br>
Noted that SARSA.py takes about 3 hours to complete the 1000 episodes, and Q_learning.py takes about 1 hours. <br>

To see the validation result, you can run validation.py to get the plot and the result. <br>

I also include baseline.py to demo how I simulate the baseline result with the environment. You can also run it to see the result.