Model Based Reinforcement Learning Approach to HVAC optimal control Repo structure agent Multi-Objective MDP formulation with objectives as thermal comfort and energy consumption Lagrangian dual reinforcement learning approach fine tuning left to do mask-agent Single objective MDP of energy consumption, and thermal comfort enforced through hard constraint action bound approach in progress: Inferring change of environment to adjust the mask accordingly base single objective reinforcement learning formulation (electric cost), with demand response demand response, Toronto Hydro electricity ToU (time of use) To reduce HVAC actuation load, CAPS action smoothing utilized Algos implemented Rainbow DQN DQN SAC PPO
