Many isolation approaches,such as zoning search,have been proposed to preserve the diversity in the decision space of mul-timodal multi-objective optimization(MMO).However,these approaches allocate the same computing resources for subspaces with dif-ferent difficulties and evolution states.In order to solve this issue,this paper proposes a dynamic resource allocation strategy(DRAS)with reinforcement learning for multimodal multi-objective optimization problems(MMOPs).In DRAS,relative contribution and im-provement are utilized to define the aptitude of subspaces,which can capture the potentials of subspaces accurately.Moreover,the rein-forcement learning method is used to dynamically allocate computing resources for each subspace.In addition,the proposed DRAS is applied to zoning searches.Experimental results demonstrate that DRAS can effectively assist zoning search in finding more and better distributed equivalent Pareto optimal solutions in the decision space.