High-speed,　low-latency　obstacle　avoidance　that　is　insensitive　to　sensor　noise　is　essential　for　enabling　multiple　decentralized　robots　to　function　reliably　in　cluttered　and　dynamic　environments.　While　other　distributed　multiagent　collision　avoidance　systems　exist,　these　systems　require　online　geometric　optimization　where　tedious　parameter　tuning　and　perfect　sensing　are　necessary.　We　present　a　novel　end-to-end　framework　to　generate　reactive　collision　avoidance　policy　for　efficient　distributed　multiagent　navigation.　Our　method　formulates　an　agent＇s　navigation　strategy　as　a　deep　neural　networkmapping　fromthe　observed　noisy　sensor　measurements　to　the　agent＇s　steering　commands　in　terms　of　movement　velocity.　We　train　the　network　on　a　large　number　of　frames　of　collision　avoidance　data　collected　by　repeatedly　running　a　multiagent　simulator　with　different　parameter　settings.　We　validate　the　learned　deep　neural　network　policy　in　a　set　of　simulated　and　real　scenarios　with　noisy　measurements　and　demonstrate　that　our　method　is　able　to　generate　a　robust　navigation　strategy　that　is　insensitive　to　imperfect　sensing　and　works　reliably　in　all　situations.　We　also　show　that　our　method　can　be　well　generalized　to　scenarios　that　do　not　appear　in　our　training　data,　including　scenes　with　static　obstacles　and　agents　with　different　sizes.