In　the　communication-based　train　control　(CBTC)　system,　traditional　modes　such　as　LTE　or　WLAN　in　train-to-train　(T2T)　communication　face　the　problem　of　a　complex　and　costly　deployment　of　base　stations　and　ground　core　networks.　Therefore,　the　multi-hop　ad　hoc　network,　which　has　the　characteristics　of　being　relatively　flexible　and　cheap,　is　considered　for　CBTC.　However,　because　of　the　high　mobility　of　the　train,　it　is　likely　to　move　out　of　the　communication　range　of　wayside　nodes.　Moreover,　some　wayside　nodes　are　heavily　congested,　resulting　in　long　packet　queuing　delays　that　cannot　meet　the　transmission　requirements.　To　solve　these　problems,　in　this　paper,　we　investigate　the　next-hop　relay　selection　problem　in　multi-hop　ad　hoc　networks　to　minimize　transmission　time,　enhance　the　network　throughput,　and　ensure　the　channel　quality.　In　addition,　we　propose　a　multiagent　dueling　deep　Q　learning　(DQN)　algorithm　to　optimize　the　delay　and　throughput　of　the　entire　link　by　selecting　the　next-hop　relay　node.　The　simulation　results　show　that,　compared　with　the　existing　routing　algorithms,　it　has　obvious　improvement　in　the　aspects　of　delay,　throughput,　and　packet　loss　rate.