面向多目标参数整定的协同深度强化学习方法

The joint optimization and tuning of multi-objective control parameters is a key issue for the automation system to maintain efficient and stable operation. Reinforcement learning is often used to establish an automated parameter adjustment agent which can replace experts to complete parameter tuning. Existing methods use fixed weights to linearly combine multiple optimization objectives into a single objective and train a single agent model with fixed tuning knowledge, making the actual objective relationship do not match the initialization, the agent can't perceive and make adaptive decision-making adjustments, limiting the effect of parameter tuning. To solve the problem, a collaborative deep reinforcement learning method was proposed for multi-objective parameter tuning. Firstly, an offline simulation was used to learn objective tuning knowledge and to establish multiple Double-DQN agents. Then tuning effect feedback was established online to perceive the actual relationship between the objectives and adjust the agents' coordination strategy to achieve effective multi-objective parameter tuning. The experimental results of automatic train operation parameter tuning show that the proposed method presents better effect on the two goals of parking error and comfort, adapting to different track performance and continue optimization, processing great practical value.