With　the　growing　demand　for　various　applications　in　intelligent　rail　transit,　the　burden　of　information　transmission　is　aggravated.　Meanwhile,　high-mobility　trains,　time-varying　channels　and　limited　package　transmission　delays　bring　great　challenges　to　the　quality　of　packet　transmission　in　train-to-train　(T2T)　communications.　In　this　paper,　to　guarantee　the　transmission　quality　and　reduce　the　deployment　cost,　wireless　ad　hoc　network　as　a　novel　technology　is　applied　to　the　communications-based　train　control　(CBTC)　system.　In　order　to　further　improve　the　transmission　efficiency　of　multi-hop　ad　hoc　networks,　a　federated　soft　actor-critic　(FeSAC)　approach　is　proposed　for　joint　optimization　of　relay　selection,　subchannel　allocation　and　power　control.　The　goal　of　the　FeSAC　algorithm　training　is　to　make　the　throughput　of　T2T　links　as　large　as　possible　with　less　energy　consumption,　while　ensuring　the　link　reliability　and　queuing　delay　requirements　of　T2T　communications.　To　synthesize　the　training　results　of　each　agents　and　accelerate　model　convergence,　the　FeSAC　algorithm　aggregates　the　network　parameters　by　calculating　the　weighted　mean　value　based　on　the　reward　of　individual　edge　intelligent　agents.　The　training　tasks　are　offloaded　to　edge　intelligent　nodes,　which　makes　parameter　aggregation　more　easily　through　interconnection.　Evaluated　by　the　simulation,　the　proposed　FeSAC　algorithm　is　more　capable　of　increasing　throughput　and　reducing　energy　consumption　compared　to　other　algorithms.