Demand Response using Deep Reinforcement Learning (DQN)|My homework helper

Posted: March 3rd, 2023

Implement this paper attached by using either python or matlab.

My conditions are:

Deadlines from 1 hour
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1- Do not use the already made framework or function call of the DQN from the AI baseline or the matlab toolbox (Do not do that), I want to build the DQN agent from scratch so I can understand it more.

2- Explain every line of code that you write so I can understand clearly, and the simpler code the better.

Please let me know If you want any article that you do not have access to so I can provide it. Also please let me know If you have any question or inquiry as you go along the work.



Demand response is a technique used by utility companies to manage electricity demand during peak hours by incentivizing customers to reduce their consumption during these times. Deep reinforcement learning (DRL) is a subset of machine learning that involves using deep neural networks to learn from trial-and-error interactions with an environment. DRL can be applied to demand response by using a technique called deep Q-network (DQN) to learn an optimal control policy for electricity consumption. The DQN agent interacts with the environment (i.e., the electricity grid) and learns to take actions that maximize its reward (i.e., minimize electricity consumption during peak hours).

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