Deep　reinforcement　learning　has　great　potential　to　acquire　complex,　adaptive　behaviors　for　autonomous　agents　automatically.　However,　the　underlying　neural　network　polices　have　not　been　widely　deployed　in　real-world　applications,　especially　in　these　safety-critical　tasks　(e.g.,　autonomous　driving).　One　of　the　reasons　is　that　the　learned　policy　cannot　perform　flexible　and　resilient　behaviors　as　traditional　methods　to　adapt　to　diverse　environments.　In　this　paper,　we　consider　the　problem　that　a　mobile　robot　learns　adaptive　and　resilient　behaviors　for　navigating　in　unseen　uncertain　environments　while　avoiding　collisions.　We　present　a　novel　approach　for　uncertainty-aware　navigation　by　introducing　an　uncertainty-aware　predictor　to　model　the　environmental　uncertainty,　and　we　propose　a　novel　uncertainty-aware　navigation　network　to　learn　resilient　behaviors　in　the　prior　unknown　environments.　To　train　the　proposed　uncertainty-aware　network　more　stably　and　efficiently,　we　present　the　temperature　decay　training　paradigm,　which　balances　exploration　and　exploitation　during　the　training　process.　Our　experimental　evaluation　demonstrates　that　our　approach　can　learn　resilient　behaviors　in　diverse　environments　and　generate　adaptive　trajectories　according　to　environmental　uncertainties.　©　2020　IEEE.