The　continuous　rise　of　inter-contented　Internet　of　Things　(IoT)　devices　has　significantly　increased　network　traffic,　complexity,　and　the　ever-changing　Internet　environment,　making　them　more　vulnerable　to　security　attacks.　Therefore,　a　robust　and　elegant　intrusion　detection　system　(IDS)　based　on　advanced　machine　learning　methods　is　required　for　securing　the　IoT　environment.　This　paper　discusses　the　new　deep　reinforcement　learning　(DRL)　based　network　intrusion　detection　system　(NIDS)　with　feature　selection　methods.　However,　the　structure　and　training　of　the　DRL　model　are　still　challenging　tasks.　Moreover,　the　effectiveness　and　accuracy　of　DRL-IDS　crucially　depend　on　the　suitable　hyper-parameters　adaptation,　i.e.,　differing　hyperparameters　can　result　in　markedly　varied　IDS　performance.　Furthermore,　due　to　the　commercial　value　of　hyper-parameters,　confidentiality　may　be　deemed　necessary,　and　proprietary　algorithms　may　protect　their　exclusive　use.　Therefore,　we　find　different　optimal　hyper-parameters　values　for　the　training　of　DRL　agents.　Furthermore,　we　evaluate　the　effectiveness　of　different　hyper-parameters　both　theoretically　and　empirically.　For　instance,　we　assess　the　hyper-parameters　for　the　case　of　varying　routing　systems　and　countermeasures　and　integrate　the　optimal　hyper-parameters　for　various　network　performances.　©　2022　IEEE.