Machine　learning　(ML)-based　methods　have　attracted　great　attention　in　the　multi-objective　optimization　prob-lems,　which　is　the　key　challenge　in　the　energy　management　strategy　(EMS)　of　the　multi-power　hybrid　system.　Our　recently　published　research　in　this　journal　verified　the　effectiveness　and　feasibility　of　a　Deep　Deterministic　Policy　Gradient　(DDPG)-based　EMS　in　the　charge-sustaining　(CS)　stage　of　a　multi-mode　plug-in　hybrid　vehicle　(PHEV).　However,　the　application　of　ML-based-EMS　in　the　charge-depletion　(CD)　stage　and　the　regenerative　braking　mode　of　PHEV　are　still　missing.　This　study　proposes　a　discrete-continuous　hybrid　actions-based　hier-archical　EMS　to　optimally　distribute　the　dual-motor　driving　force　in　battery　electric　driving　and　regenerative　braking.　In　the　upper　layer　of　EMS,　DDPG　is　trained　to　learn　the　torque　distribution　principles　of　dual-motor　operation　to　achieve　better　energy　efficiency　without　losing　dynamic　performance.　Meanwhile,　the　total　recoverable　braking　torque　is　also　determined　by　the　upper　layer　EMS　considering　the　braking　demand,　me-chanical　and　electrical　braking　system　conditions,　vehicle　safety,　and　the　provisions　of　law.　In　the　lower　level　of　EMS,　the　driving　mode　is　determined　under　the　guidance　of　energy　consumption　optimization.　The　verified　results　show　that　the　proposed　EMS　outperforms　other　deep　reinforcement　learning　(DRL)-based　hierarchical　and　non-hierarchical　EMSs.