In　this　paper,　we　investigate　an　image　restoration　method　based　on　a　deep　reinforcement　learning　framework,　aiming　to　recover　low　quality　images　to　high　quality　images.　The　general　deep　learning-based　approach　trains　a　single　large　network　to　accomplish　a　specific　task,　which　is　difficult　to　handle　when　facing　mixed　distorted　images.　To　address　this　problem,　we　propose　the　residual　Double　DQN　algorithm,　which　introduces　the　idea　of　residuals　into　the　deep　reinforcement　learning　framework.　The　agent　learns　a　policy　to　select　appropriate　actions　from　the　action　set　to　gradually　restore　the　quality　of　mixed　distorted　images.　The　framework　uses　the　residual　blocks　to　improve　the　feature　extraction　ability　of　the　agent,　so　as　to　guide　it　to　adaptively　select　the　appropriate　action　for　image　recovery.　In　addition,　based　on　the　the　new　reward　function　which　is　designed　based　on　human-eye　inspiration,　the　framework　can　handle　the　mixed　distortion　of　images　containing　noise,　blur,　and　JPEG　compression　at　the　same　time.　The　experimental　results　show　that　our　proposed　model　has　low　complexity　and　is　superior　to　the　existing　methods　in　processing　mixed　distortion　images.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.