In　this　paper,　a　novel　parallel　learning　framework　is　developed　to　solve　zero-sum　games　for　discrete-time　nonlinear　systems.　Briefly,　the　purpose　of　this　study　is　to　determine　a　tentative　function　according　to　the　prior　knowledge　of　the　value　iteration　(VI)　algorithm.　The　learning　process　of　the　parallel　controllers　can　be　guided　by　the　tentative　function.　That　is　to　say,　the　neighborhood　of　the　optimal　cost　function　can　be　compressed　within　a　small　range　via　two　typical　exploration　policies.　Based　on　the　parallel　learning　framework,　a　novel　dichotomy　VI　algorithm　is　established　to　accelerate　the　learning　speed.　It　is　shown　that　the　parallel　controllers　will　converge　to　the　optimal　policy　from　contrary　initial　policies.　Finally,　two　typical　systems　are　used　to　demonstrate　the　learning　performance　of　the　constructed　dichotomy　VI　algorithm.　©　2023　Elsevier　Ltd