This　paper　investigates　the　multivariable　control　of　wastewater　treatment　processes　(WWTP).　This　paper　integrates　deep　reinforcement　learning　(DRL)　with　PID　control　and　proposes　a　multivariable　adaptive　PID　control　strategy　based　on　multi-agent　DRL　(MADRL)　for　WWTP.　The　approach　begins　with　the　construction　of　a　MADRL-PID　controller　structure,　consisting　of　an　agent　and　a　PID　controller　module.　The　agent　adjusts　the　PID　controller　values　while　the　PID　module　calculates　the　control　signal.　To　enhance　the　agent＇s　ability　to　cooperatively　tune　multiple　PID　controllers,　the　algorithm＇s　components–reward　function,　action　space,　environment,　and　state　space–are　designed　according　to　the　BSM1　simulation　platform　principles　and　the　MADRL　framework　requirements.　Additionally,　to　handle　WWTP＇s　non-linearities,　uncertainties,　and　parameter　coupling,　the　multi-agent　deep　deterministic　policy　gradient　algorithm　is　selected　as　the　foundation　for　training　the　agents.　Experimental　results　demonstrate　that　the　proposed　algorithm　exhibits　greater　adaptability　than　traditional　PID　control　and　achieves　superior　control performance.　©　2025　The　Author(s).　IET　Control　Theory　&　Applications　published　by　John　Wiley　&　Sons　Ltd　on　behalf　of　The　Institution　of　Engineering　and　Technology.