Classical　building　control　systems　are　becoming　vulnerable　with　increasing　complexities　in　contemporary　built　environments　and　energy　systems.　Due　to　this,　the　reinforcement　learning　(RL)　method　is　becoming　more　distinctive　and　applicable　in　control　networks　for　buildings.　This　paper,　therefore,　conducts　a　comprehensive　review　of　RL　techniques　applied　in　control　systems　for　occupant　comfort　in　indoor　built　environments.　The　empirical　applications　of　RL-based　control　systems　are　presented,　depending　on　comfort　objectives　(thermal　comfort,　indoor　air　quality,　and　lighting)　along　with　other　objectives　which　invariably　includes　energy　consumption.　The　class　of　RL　algorithms　and　implementation　details　regarding　how　the　value　functions　have　been　represented　and　how　the　policies　are　improved　are　also　illustrated.　This　paper　shows　there　are　limited　works　for　which　RL　has　been　explored　for　controlling　occupant　comfort,　especially　in　indoor　air　quality　and　lighting.　Relatively　few　of　the　reviewed　works　incorporate　occupancy　patterns　and/or　occupant　feedback　into　the　control　loop.　Moreover,　this　paper　identifies　a　gap　with　regard　to　the　performance　of　implementing　cooperative　multi-agent　RL　(MARL).　Based　on　our　findings,　current　challenges　and　further　opportunities　are　discussed.　We　expect　to　clarify　the　feasible　theory　and　functions　of　RL　for　building　control　systems,　which　would　promote　their　wider-spread　application　in　built　environments.