This　study　investigates　the　multi-objective　path　planning　problem　in　logistics　autonomous　systems　(LAS),　where　unmanned　ground　vehicles　(UGVs)　need　to　deliver　multiple　packages　to　various　destinations　while　avoiding　obstacles.　Using　the　null-space-based　behavioral　control　(NSBC)　framework,　we　extend　our　previous　reinforcement　learning　task　supervisor　(RLTS)　to　propose　an　enhanced　RLTS　(IRLTS)　for　optimal　path　planning　and　dynamic,　simultaneous　task　priority　adjustment.　Notably,　IRLTS　can　re-order　delivery　to　minimize　total　path　length　when　presented　with　unknown　obstacles.　Simulations　affirm　that　IRLTS　achieves　shorter　total　path　lengths　than　RLTS　and　outperforms　offline　optimization-based　algorithms　on　path　length　and　re-planning　capability.　Copyright　(c)　2023　The　Authors.　This　is　an　open　access　article　under　the　CC　BY-NC-　ND　license　(https://creativecommons.org/licenses/by-nc-nd/4.0/)