The　operation　of　heating,　ventilation,　and　air　conditioning　(HVAC)　systems　in　modern　buildings　often　suffers　due　to　the　limited　adaptability　and　efficiency　of　conventional　rule-based　control　mechanisms.　Addressing　the　need　for　more　rational　and　flexible　operation,　we　introduce　a　novel　multi-objective　optimization　approach　leveraging　Deep　Q-Network　(DQN)　for　open　office　environments.　Our　method　aims　to　strike　an　optimal　balance　between　minimizing　energy　consumption　and　maximizing　occupant　comfort　within　the　air　conditioning　system.　Unlike　rule-based　methods,　our　data-driven　approach　circumvents　the　need　for　extensive　a　priori　knowledge　of　building-specific　thermodynamics,　thereby　enhancing　adaptability　and　ease　of　deployment.　We　developed　a　building　simulation　model　for　the　target　open　office　area,　complemented　by　a　custom-built　simulation　and　testing　platform　integrated　with　a　building　modeling　interface.　This　platform　facilitates　real-time　data　acquisition　and　parameter　adjustment,　enabling　the　DQN　agent　to　efficiently　control　HVAC　operations.　By　dynamically　adjusting　temperature　setpoints　across　various　thermal　zones　via　variable　air　volume　(VAV)　actuators,　the　DQN　agent　skillfully　navigates　the　energy-occupant　comfort　trade-off.　Empirical　evidence　from　extensive　training　iterations　indicates　that　our　DQN-based　control　method　outperforms　traditional　rule-based　systems,　reducing　energy　consumption　by　up　to　24%　while　maintaining　optimal　occupant　comfort.　The　experimental　results　demonstrate　the　efficacy　of　the　proposed　method　for　energy　optimization　in　building　management.