Ship　detection　plays　an　important　role　in　automatic　remote　sensing　image　interpretation.　The　scale　difference,　large　aspect　ratio　of　ship,　complex　remote　sensing　image　background　and　ship　dense　parking　scene　make　the　detection　task　difficult.　To　handle　the　challenging　problems　above,　we　propose　a　ship　rotation　detection　model　based　on　a　Feature　Fusion　Pyramid　Network　and　deep　reinforcement　learning　(FFPN-RL)　in　this　paper.　The　detection　network　can　efficiently　generate　the　inclined　rectangular　box　for　ship.　First,　we　propose　the　Feature　Fusion　Pyramid　Network　(FFPN)　that　strengthens　the　reuse　of　different　scales　features,　and　FFPN　can　extract　the　low　level　location　and　high　level　semantic　information　that　has　an　important　impact　on　multi-scale　ship　detection　and　precise　location　of　dense　parking　ships.　Second,　in　order　to　get　accurate　ship　angle　information,　we　apply　deep　reinforcement　learning　to　the　inclined　ship　detection　task　for　the　first　time.　In　addition,　we　put　forward　prior　policy　guidance　and　a　long-term　training　method　to　train　an　angle　prediction　agent　constructed　through　a　dueling　structure　Q　network,　which　is　able　to　iteratively　and　accurately　obtain　the　ship　angle.　In　addition,　we　design　soft　rotation　non-maximum　suppression　to　reduce　the　missed　ship　detection　while　suppressing　the　redundant　detection　boxes.　We　carry　out　detailed　experiments　on　the　remote　sensing　ship　image　dataset,　and　the　experiments　validate　that　our　FFPN-RL　ship　detection　model　has　efficient　detection　performance.