In　the　domain　of　multi-agent　pathfinding　within　the　framework　of　partially　observable　Markov　decision　processes,　the　existing　research　mainly　focuses　on　grid　or　particle　environments,　which　are　far　from　the　real-world　physical　environments.　This　paper　delves　into　enhancing　the　performance　of　collaborative　multi-agent　pathfinding　in　environments　closer　to　actual　physical　constraints.　In　consideration　of　the　realities　of　physical　limitations,　a　multiple　constraint　action　space　that　accounts　for　actuator　saturation　and　underactuation　is　constructed.　Concurrently,　a　multisource　input　state　space　based　on　distance　and　spatial　coordinates　is　developed.　Furthermore,　an　anti-redundancy　reward　function　is　designed　to　reduce　the　redundancy　of　the　actions　during　the　navigational　processes　of　unmanned　vehicles.　Moreover,　in　dealing　with　the　challenges　of　elevated　training　complexity,　suboptimal　efficiency,　and　convergence　difficulties　within　the　Gazebo　simulation　environment,　we　propose　a　pre-training　and　fine-tuning　based　multi-agent　twinned　delayed　deep　deterministic　policy　gradient　algorithm.　This　method　leverages　pre-training　to　confer　upon　the　model　a　more　optimal　initial　state　to　improve　the　training　efficiency.　Subsequently,　fine-tuning　is　employed　to　refine　the　pre-trained　model,　which　further　enhances　the　model＇s　resilience　to　the　non-stationary　environment　during　the　training　phase.　In　the　Gazebo　simulation　environment,　the　effectiveness　of　the　proposed　algorithm　is　verified　by　comparing　it　with　the　algorithms　such　as　PMATD3,　MATD3　and　MADDPG.　©　2025　Northeast　University.　All　rights　reserved.