An　occupant＇s　window　opening　and　closing　behaviour　can　significantly　influence　the　level　of　comfort　in　the　indoor　environment.　Such　behaviour　is,　however,　complex　to　predict　and　control　conventionally.　This　paper,　therefore,　proposes　a　novel　reinforcement　learning　(RL)　method　for　the　advanced　control　of　window　opening　and　closing.　The　RL　control　aims　at　optimising　the　time　point　for　window　opening/closing　through　observing　and　learning　from　the　environment.　The　theory　of　model-free　RL　control　is　developed　with　the　objective　of　improving　occupant　comfort,　which　is　applied　to　historical　field　measurement　data　taken　from　an　office　building　in　Beijing.　Preliminary　testing　of　RL　control　is　conducted　by　evaluating　the　control　method＇s　actions.　The　results　show　that　the　RL　control　strategy　improves　thermal　and　indoor　air　quality　by　more　than　90%　when　compared　with　the　actual　historically　observed　occupant　data.　This　methodology　establishes　a　prototype　for　optimally　controlling　window　opening　and　closing　behaviour.　It　can　be　further　extended　by　including　more　environmental　parameters　and　more　objectives　such　as　energy　consumption.　The　model-free　characteristic　of　RL　avoids　the　disadvantage　of　implementing　inaccurate　or　complex　models　for　the　environment,　thereby　enabling　a　great　potential　in　the　application　of　intelligent　control　for　buildings.