Purpose:　The　formulation　and　optimization　of　radiation　therapy　plans　are　complex　and　time-consuming　processes　that　heavily　rely　on　the　expertise　of　medical　physicists.　Consequently,　there　is　an　urgent　need　for　automated　optimization　methods.　Recent　advancements　in　reinforcement　learning,　particularly　deep　reinforcement　learning　(DRL),　show　great　promise　for　automating　radiotherapy　planning.　This　review　summarizes　the　current　state　of　DRL　applications　in　this　field,　evaluates　their　effectiveness,　and　identifies　challenges　and　future　directions.　Methods:　A　systematic　search　was　conducted　in　Google　Scholar,　PubMed,　IEEE　Xplore,　and　Scopus　using　keywords　such　as　＂deep　reinforcement　learning＂,　＂radiation　therapy＂,　and　＂treatment　planning＂.　The　extracted　data　were　synthesized　for　an　overview　and　critical　analysis.　Results:　The　application　of　deep　reinforcement　learning　in　radiation　therapy　plan　optimization　can　generally　be　divided　into　three　categories:　optimizing　treatment　planning　parameters,　directly　optimizing　machine　parameters,　and　adaptive　radiotherapy.　From　the　perspective　of　disease　sites,　DRL　has　been　applied　to　cervical　cancer,　prostate　cancer,　vestibular　schwannoma,　and　lung　cancer.　Regarding　types　of　radiation　therapy,　it　has　been　used　in　HDRBT,　IMRT,　SBRT,　VMAT,　GK,　and　Cyberknife.　Conclusions:　Deep　reinforcement　learning　technology　has　played　a　significant　role　in　advancing　the　automated　optimization　of　radiation　therapy　plans.　However,　there　is　still　a　considerable　gap　before　it　can　be　widely　applied　in　clinical　settings　due　to　three　main　reasons:　inefficiency,　limited　methods　for　quality　assessment,　and　poor　interpretability.　To　address　these　challenges,　significant　research　opportunities　exist　in　the　future,　such　as　constructing　evaluators,　parallelized　training,　and　exploring　continuous　action　spaces.