关键词: 强化学习, 并行强化学习, 经验回放, 多线程技术, 建筑节能

Abstract: This paper proposes a method of building energy conservation based on parallel reinforcement learning, which combines a multi-threading technique and the experiment replay method to propose a multi-threading parallel reinforcement learning algorithm framework. Its novelty lies in that it introduces the self-simulation metric method in the experience replay process, which selects samples with low similarity by calculating the distance between samples to construct multiple sample pool. And in the learning process, Agent selects samples from the multiple sample pool to effectively avoid wasting learning resources. The experiments include the comparison experiments with the Q-Learning algorithm and the PID method on the simulation room model. Experimental results show that the proposed parallel algorithm has faster learning rate and convergence rate, and it can solve the optimal policy faster with higher operating efficiency.

Key words: reinforcement learning, parallel reinforcement learning, experiment replay, multi-threading technology, building conversation