Comparing the performance of Reinforcement learning models to control temperature with that of a PID and a thermostat controller. Find video of the training process here.
Find the Google Slides Link to the project presentation here.
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Open the DDPG Folder and Run sldemo_househeat_data.m, and make sure variables exist on the workspace.
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Run house_thermostat.slx to generate a the plots for the control using a regular thermostat.
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Run house_PID.slx to generate a the plots for the control using a Discrete PID controller.
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ddpg_live(new).mlx live notebook. Start running each cell individually. (Make sure the variable - training is set to true in the notebook.)
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Open the Final Folder and first Run sldemo_househeat_data.m, rlwatertank.slx again, pg.m to generate a the plots for the control using a Policy Gradient. DDPG.m to generate a the plots for the control using a Deep Deterministic Policy Gradient. Td3.mto generate a the plots for the control using a Twin Delayed Deep Deeterministic Policy Gradient.
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Open the DQN Folder and run first Run sldemo_househeat_data.m, rlwatertank.slx again, dqn.m to generate a the plots for the control using a Deep Q Network.
Make sure to have the following toolkits installed to be able to recreate these simulations successfully:
- Reinforcement Learning Toolkit.
- Machine Learning Toolkit.
- PID Tuner
You will be able to tune the reward function for the simulation by updating the Reward block in the RL_Heat_DDPG_test.slx file.
Use this link to set up base thermal model of the house from the MATLAB-SIMULINK website.