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This thesis aims to address the rising energy costs by using IoT technology and reinforcement learning. We use historical sensor data to fit a deep reinforcement learning model that is capable of optimizing the control of a heating system in a way that minimizes energy costs, while maintaining a comfortable indoor temperature. This model-free approach uses neural networks to simulate the thermodyn
