Hybrid　Software　Defined　Networks　(Hybrid　SDNs),　which　combines　the　robustness　of　distributed　network　and　the　flexibility　of　centralized　network,　is　now　a　prevailing　network　architecture.　Previous　hybrid　SDN　Traffic　Engineering　(TE)　solutions　search　an　optimal　link　weight　setting　or　compute　the　splitting　ratios　of　traffic　leveraging　heuristic　algorithms.　However,　these　methods　cannot　react　timely　to　the　fluctuating　traffic　demands　in　dynamic　environments　and　suffer　a　hefty　performance　degradation　when　traffic　demands　change　or　network　failures　happen,　especially　when　network　scale　is　large.　To　cope　with　this,　we　propose　a　Multi-Agent　reinforcement　learning　based　TE　method　MATE　that　timely　determines　the　route　selection　for　network　flows　in　dynamic　hybrid　SDNs.　Through　dividing　the　large-scale　routing　optimization　problem　into　small-scale　problem,　MATE　can　better　learn　the　mapping　between　the　traffic　demands　and　routing　policy,　and　efficiently　make　online　routing　inference　with　dynamic　traffic　demands.　To　collaborate　multiple　agents　and　speed　up　the　convergence　in　the　training　process,　we　innovatively　design　the　actor　network　and　introduce　previous　actions　of　all　agents　in　the　training　of　each　agent.　Extensive　experiments　conducted　on　different　network　topologies　demonstrate　our　proposed　method　MATE　has　superior　TE　performance　with　dynamic　traffic　demands　and　is　robust　to　network　failures.　©　2024　Elsevier　Ltd