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Convergent Actor-Critic Algorithms Under Off-Policy Training and Function Approximation
We present the first class of policy-gradient algorithms that work with both state-value and policy function-approximation, and are guaranteed to converge under off-policy training.
Optimal Demand Response Using Device Based Reinforcement Learning
Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated Energy Management System (EMS) is a necessary prerequisite to DR in these areas.
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