The tightening Carbon Intensity Indicator (CII) regulations has brought challenges to the maritime industry. For a huge number of aging ships, retrofit is vital for stakeholders to achieve International Maritime Organization (IMO)’s targeted carbon emission reduction of 20-30% by 2030. At present, the retrofitting approaches generally includes wind assisted ship propulsion, holistic hydrodynamic optimization, air lubrication biofouling management, electrification, low carbon emission fuel, etc. No single retrofitting approach could achieve the reduction goal presently. Therefore, a combined retrofit policy is crucial for each ship in demand. Considering that the requirements of CII regulations is tightening gradually instead of cutting 20-30% carbon emissions immediately, adopting optimal retrofit approaches at the right time based on ship’s decarbonization conditions is vital for stakeholders. Deep reinforcement learning (DRL) is introduced to acquire the optimal ship retrofit policy for lowest life-cycle cost under the supervision of CII regulations. Herein, a DRL-driven ship retrofit policy optimization framework based on CII conditions is established to improve ships’ market competitiveness in the context of carbon reduction.