To　address　the　issue　that　urban　environments　often　make　command　systems　inefficient　and　inflexible　due　to　their　geospatial　complexity　and　dynamic　changes,　a　multi-agent　cooperative　confrontation　method　with　proximal　policy　optimization　for　urban　environments　was　proposed.　First,　on　the　basis　of　establishing　a　comprehensive　urban　confrontation　environment,　the　AC　(actor-critic)　network　with　proximal　policy　optimization　was　used　to　solve　the　problem.　Then,　aiming　at　the　multi-to-one　critic　network,　an　embedding　method　was　adopted　to　address　the　issue　of　evaluating　the　decision-making　of　heterogeneous　agents　with　different　spatial　dimensions.　Furthermore,　adaptive　sampling　was　added　to　assist　in　the　updating　of　proximal　policy　optimization.　Finally,　the　weights　of　the　actor　network　were　inherited　to　help　agents　quickly　take　over　the　corresponding　tasks.　Experimental　results　show　that　the　proposed　method　improves　22.67%　reward　and　8.14%　convergence　rate　compared　to　other　methods,　which　not　only　meets　the　decision-making　of　multiple　agents’cooperative　confrontation　in　urban　environments,　but　also　is　compatible　with　the　cooperative　confrontation　of　multiple　heterogeneous　agents.　©　2025　Editorial　Board　of　Journal　on　Communications.　All　rights　reserved.