In　recent　years,　using　functional　magnetic　resonance　imaging　(fMRI)　data　to　infer　brain　effective　connectivity　(EC)　between　different　brain　regions　is　an　important　advanced　study　in　neuroinformatics.　However,　current　methods　always　perform　not　well　due　to　the　high　noise　of　neuroimaging　data.　In　this　paper,　we　propose　an　effective　connectivity　learning　method　with　deep　reinforcement　learning,　called　EC-DRL,　aiming　to　more　accurately　identify　the　brain　effective　connectivity　from　fMRI　data.　The　proposed　method　is　based　on　the　actor-critic　algorithm　framework,　using　the　encoder-decoder　model　as　the　actor　network.　More　specifically,　the　encoder　adopts　the　Transformer　model　structure,　and　the　decoder　uses　a　bidirectional　long-short-term　memory　network　with　an　attention　mechanism.　A　large　number　of　experimental　results　on　simulated　fMRI　data　and　real-world　fMRI　data　show　that　EC-DRL　can　better　infer　effective　connectivity　compared　to　the　state-of-the-art　methods.　©　2022　IEEE.