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.