After　a　fire　occurs,　it　is　imperative　that　people　in　danger　evacuate　as　soon　as　possible.　However,　the　current　emergency　plan　based　on　the　pre-established　static　exiting　route　is　unable　to　considering　the　real-time　fire　scenario.　In　addition,　the　selection　of　evacuation　routes　significantly　relies　on　the　decision-maker＇s　experiences.　These　issues　seriously　affect　evacuation　efficiency,　decreasing　the　likelihood　of　survival.　This　paper　developed　an　effective　real-time　evacuation　guidance　method　that　can　automatically　select　the　evacuation　route　in　accordance　with　real-time　fire　scenarios.　The　model　is　established　based　on　the　on-policy　learning　algorithm　SARSA　(State-action-reward-state-action),　an　algorithm　for　learning　a　Markov　decision　process　policy,　which　could　mimic　the　decision-making　of　pedestrian　behaviors　in　an　emergency.　In　addition,　two　types　of　radar　(exit　radar　and　fire　radar)　are　introduced　into　the　SARSA　algorithm　to　facilitate　the　wayfinding　process,　which　formulated　the　so-called　Radar-assisted　SARSA　(RSARSA).　The　results　have　shown　that　RSARSA　can　swiftly　decide　a　safer　evacuation　route　for　pedestrians　or　crowd　at　arbitrary　location.　The　convergence　time　of　initial　successful　route　planning　is　between　0.05　and　4.5　s　under　the　tests　in　this　paper.　The　evacuation　route　determined　by　this　algorithm　can　well　consider　the　fire,　and　timely　avoid　routes　with　potential　dangerous.　Moreover,　RSARSA　can　flexibly　respond　to　different　fires　under　various　heat　release　rates　and　development　speeds.　By　applying　this　technology,　fire　evacuation　can　be　guided　by　routes　that　are　more　attuned　to　the　mindset　of　pedestrians.　It　can　provide　a　good　basis　for　route　selection　of　crowd　evacuation.