Due　to　manufacturing　defects,　chip　aging,　and　potential　malicious　attacks,　unexpected　errors　may　occur　in　the　valves　of　Fully　Programmable　Valve　Array　(FPVA)　biochips.　To　address　this　issue,　an　error　recovery　method　based　on　Deep　Reinforcement　Learning　(DRL)　for　FPVA　biochips　to　handle　valve-related　unexpected　errors　is　proposed,　which　involves　designing　specific　error　recovery　operations　for　different　error　types,　introducing　a　sequencing　graph　adjustment　method　to　generate　error　recovery　sequencing　graph,　and　designing　a　resynthesis　method　to　realize　error　recovery.　The　resynthesis　method　contains　a　priority-based　scheduling　adjustment,　a　DRL-based　placement　adjustment,　and　a　DRL-based　routing　adjustment,　which　aims　at　updating　the　execution　timetable　for　operations,　component　placements,　and　fluid　transport　paths.　The　model　parameters　are　updated　using　a　proximal　policy　optimization　algorithm,　continually　learning　from　a　large　number　of　randomly　simulated　error　scenarios,　resulting　in　strong　generalization　performance.　In　comparison　to　existing　work,　the　proposed　method　achieves　lower　probability　of　error　recovery　failure,　shorter　completion　time　of　bioassay,　and　faster　runtime.