Distinguishing　the　manifestations　of　pulmonary　nodules　poses　a　significant　challenge　in　the　medical　field,　demanding　the　expertise　of　experienced　radiologists.　This　complexity　results　in　the　high　cost　and　inadequacy　of　sample　labeling.　In　response,　this　study　introduces　a　reinforcement　learning　approach,　denoted　as　TriCaps-RL,　for　the　classification　of　pulmonary　nodules　based　on　CT　signs.　This　approach　initially　employs　the　Q-value　loss　to　train　a　single　CapsNet.　When　the　performance　of　the　CapsNet　plateaus　and　further　enhancement　becomes　challenging,　the　loss　function　is　progressively　replaced　by　a　triplet　metric　to　augment　performance.　Consequently,　a　more　nuanced　differentiation　among　various　types　of　pulmonary　nodules　is　achieved.　The　proposed　method　can　improve　its　performance　through　interactions　with　radiologists　during　their　CT　image　reading　process,　thereby　mitigating　the　shortage　of　radiologists　and　addressing　the　time-consuming　issue　of　training　sample　labeling　in　medical　image　research.　This　paper　delves　into　the　fine-grained　classification　of　pulmonary　nodules　using　deep　reinforcement　learning　based　on　CT　imaging　signs.　In　comparison　to　previous　studies　that　primarily　concentrate　on　the　benign/malignant　classification　of　lung　nodules,　this　fine-grained　classification　of　nodules　founded　on　CT　imaging　signs　proves　to　be　more　valuable　for　medical　practitioners　in　making　accurate　diagnostic　decisions.　©　2024,　Taiwan　Ubiquitous　Information　CO　LTD.　All　rights　reserved.