Sparse　mobile　crowdsensing　is　a　cost-effective　sensing　paradigm　that　infers　global　data　by　sensing　data　from　partial　areas　in　a　city.　With　the　rapid　development　of　diverse　autonomous　mobile　agents　such　as　unmanned　aerial　vehicles　(UAVs)　and　ground　vehicles,　they　have　been　widely　applied　in　sparse　mobile　crowdsensing.　However,　existing　works　often　predefine　the　role　structures　and　behavioral　preferences　of　these　agents　in　tasks,　which　significantly　limits　their　flexibility　and　adaptability,　and　making　it　difficult　to　fully　exploit　the　collaborative　potential　of　crowdsensing　agents　to　efficiently　achieve　high-quality　data　sensing.　In　this　paper,　we　propose　an　adaptive　role　learning　framework　for　sparse　mobile　crowdsensing　(ARL-SMCS),　which　focuses　on　role　recognition　for　heterogeneous　agents　and　role　refinement　among　homogeneous　agents.　This　framework,　based　on　a　multi-agent　reinforcement　learning　model,　introduces　a　variational　autoencoder　to　learn　the　latent　role　representations　of　agents　and　uses　maximum　mean　discrepancy　to　distinguish　the　functionalities　of　different　types　of　agents.　Additionally,　ARL-SMCS　incorporates　an　evolutionary　algorithm　to　further　refine　task　preferences　among　homogeneous　agents.　This　framework　overcomes　the　limitations　of　static　role　assignment　in　adapting　to　dynamic　environments　and　task　conflicts　during　task　execution,　significantly　improving　sensing　quality　and　resource　utilization　efficiency.　Extensive　experiments　on　two　real-world　datasets　demonstrate　that　ARL-SMCS　consistently　outperforms　other　baseline　methods　under　various　conditions,　including　different　numbers,　endurance,　and　decision　interval　lengths.　©　2014　IEEE.