In　recent　years,　with　the　continuous　release　of　HTTP　adaptive　streaming　(HAS)　video　datasets　and　network　trace　datasets,　the　machine　learning　methods,　such　as　deep　learning　and　reinforcement　learning,　have　been　continuously　applied　to　adaptive　bit　rate　(ABR)　algorithms,　which　obtain　the　optimal　strategy　of　rate　control　through　interactive　learning,　and　achieve　superior　performance　that　surpasses　the　traditional　heuristic　methods.　Based　on　the　analysis　of　the　research　difficulties　of　ABR　algorithms,　the　research　advances　of　ABR　algorithms　based　on　reinforcement　learning　(including　deep　reinforcement　learning)　was　investigated.　Furthermore,　several　representative　HAS　video　datasets　and　network　trace　datasets　were　summarized,　the　evaluation　metrics　of　the　performance　were　depicted.　Finally,　the　existing　problems　and　the　future　tendency　of　ABR　research　were　discussed.　©　2021,　Editorial　Board　of　Journal　on　Communications.　All　right　reserved.