In　this　article,　an　adjustable　behavior-guided　adaptive　dynamic　programming　(BGADP)　algorithm　is　designed　to　solve　the　optimal　regulation　problem　for　discrete-time　systems.　In　conventional　adaptive　dynamic　programming　methods,　gradient　information　of　system　dynamics　is　necessary　for　conducting　policy　improvement.　However,　these　methods　face　challenges　when　gradient　information　cannot　be　computed　or　when　the　system　dynamics　is　non-differentiable.　To　overcome　these　limitations,　a　human-behavior-inspired　swarm　intelligence　approach　is　used　to　search　for　superior　policies　during　the　iterative　process,　eliminating　the　need　for　gradient　information.　Additionally,　a　relaxation　factor　is　introduced　into　the　value　function　update　to　accelerate　the　convergence　speed　of　the　algorithm.　The　monotonicity　and　convergence　properties　of　the　iterative　value　function　are　rigorously　analyzed.　Finally,　the　effectiveness　and　practicality　of　the　adjustable　BGADP　algorithm　are　validated　through　two　simulation　studies,　which　are　implemented　using　the　actor-critic　framework　with　neural　networks.