Washing Optimization Method Based on Deep Reinforcement Learning for Fully Programmable Valve Array Biochips

In recent years, Fully Programmable Valve Array (FPVA) has emerged as a promising alternative for microfluidic biochips with flexible features. When two fluids flow sequentially through the same microchannel, the latter will be contaminated by the former’s residues. To solve the contamination problem, the buffer should be injected into microchannel to wash the contaminated area before reusing the microchannel. Considering the buffer capacity limitation, this paper proposes a washing optimization method based on Deep Reinforcement Learning (DRL), which aims to minimize the washing time and buffer washing capacity. First, a preprocessing method for the washing optimization problem is designed to generate the inputs for the initial state of the DRL environment based on the given physical design scheme. Second, an FPVA biochip simulation environment is constructed. In addition, the corresponding state, action space and high-precision reward function are designed. Finally, a new washing optimization framework is formulated based on DRL, which adopts the Proximal Policy Optimization (PPO) algorithm and Convolutional Neural Networks (CNN) to implement the washing decision. Experimental results on several benchmarks show that the proposed washing optimization method can further reduce the washing time and buffer washing capacity in comparison with related work.