Improving　the　resilience　of　urban　road　networks　suffering　from　various　disruptions　has　been　a　central　focus　for　urban　emergence　management.　However,　to　date　the　effective　methods　which　may　mitigate　the　negative　impacts　caused　by　the　disruptions,　such　as　road　accidents　and　natural　disasters,　on　urban　road　networks　is　highly　insufficient.　This　study　proposes　a　novel　adaptive　signal　control　strategy　based　on　a　doubly　dynamic　learning　framework,　which　consists　of　deep　reinforcement　learning　and　day-to-day　traffic　dynamic　learning,　to　improve　the　network　performance　by　adjusting　red/green　time　split.　In　this　study,　red　time　split　is　regarded　as　extra　traffic　flow　to　discourage　drivers　to　use　affected　roads,　so　as　to　reduce　congestion　and　improve　the　resilience　when　urban　road　networks　are　subject　to　different　levels　of　disruptions.　In　addition,　we　utilize　the　convolution　neural　network　as　Q-network　to　approximate　Q　values,　link　flow　distribution　and　link　capacity　are　regarded　as　the　state　space,　and　actions　are　denoted　as　red/green　time　split.　A　small　network　is　utilized　as　a　numerical　example,　and　a　fixed　time　signal　control　and　other　two　adaptive　signal　controls　are　employed　for　the　comparisons　with　the　proposed　one.　The　results　show　that　the　proposed　adaptive　signal　control　based　on　deep　reinforcement　learning　can　achieve　better　resilience　in　most　of　the　cases,　particularly　in　the　scenarios　of　moderate　and　severe　disruptions.　This　study　may　shed　light　on　the　advantages　of　the　proposed　adaptive　signal　control　dealing　with　major　emergencies　compared　to　others.