With　the　rapid　development　of　cloud　computing　technologies,　more　and　more　individual　users　and　enterprises　choose　to　deploy　their　key　applications　in　green　data　centers　(GDCs),　and　the　scale　of　GDCs　is　increasing　rapidly.　To　ensure　service　quality　and　maximize　the　revenue,　cloud　service　providers　in　GDCs　need　to　reasonably　and　efficiently　allocate　computing　resources　and　schedule　tasks　of　users.　Traditional　heuristic　algorithms　face　challenges　of　uncertainty　and　complexity　in　GDCs　for　scheduling　tasks.　To　solve　them,　this　work　establishes　an　improved　resource　allocation　and　task　scheduling　method　based　on　deep　reinforcement　learning.　It　considers　the　dependency　among　different　tasks,　and　builds　a　workload　model　based　on　the　real-life　data　in　Google　cluster　trace.　In　addition,　a　deep　reinforcement　learning-based　scheduling　model　is　proposed　to　reasonably　allocate　and　schedule　resources　(CPU　and　memory)　in　GDCs.　Based　on　two　models,　an　Improved　Deep　Q-learning　Network　(IDQN)　is　proposed　to　autonomously　learn　the　changing　environment　of　GDCs,　and　yield　the　optimal　strategy　for　resource　allocation　and　task　scheduling.　Real-life　data-based　experiments　demonstrate　that　IDQN　achieves　lower　task　rejection　rates　and　energy　cost　than　several　typical　task　scheduling　methods.　　©　2022　IEEE.