Traditional　image　retrieval　methods　often　face　challenges　in　adapting　to　varying　user　preferences　and　dynamic　datasets.　To　address　these　limitations,　this　research　introduces　a　novel　image　retrieval　framework　utilizing　deep　deterministic　policy　gradients　(DDPG)　augmented　with　a　self-adaptive　reward　mechanism　(SARM).　The　DDPG-SARM　framework　dynamically　adjusts　rewards　based　on　user　feedback　and　retrieval　context,　enhancing　the　learning　efficiency　and　retrieval　accuracy　of　the　agent.　Key　innovations　include　dynamic　reward　adjustment　based　on　user　feedback,　context-aware　reward　structuring　that　considers　the　specific　characteristics　of　each　retrieval　task,　and　an　adaptive　learning　rate　strategy　to　ensure　robust　and　efficient　model　convergence.　Extensive　experimentation　with　the　three　distinct　datasets　demonstrates　that　the　proposed　framework　significantly　outperforms　traditional　methods,　achieving　the　highest　retrieval　accuracy　having　3.38%,　5.26%,　and　0.21%　improvement　overall　as　compared　to　the　mainstream　models　over　DermaMNIST,　PneumoniaMNIST,　and　OrganMNIST　datasets,　respectively.　The　findings　contribute　to　the　advancement　of　reinforcement　learning　applications　in　image　retrieval,　providing　a　user-centric　solution　adaptable　to　various　dynamic　environments.　The　proposed　method　also　offers　a　promising　direction　for　future　developments　in　intelligent　image　retrieval　systems.