Modern　processors　employ　data　prefetchers　to　alleviate　the　impact　of　long　memory　access　latency.　However,　current　prefetchers　are　designed　for　specific　memory　access　patterns,　which　perform　poorly　on　mixed　applications　with　multiple　memory　access　patterns.　To　address　these　issues,　RL-CoPref,　a　reinforcement　learning　(RL)-based　coordinated　prefetching　controller　for　multiple　prefetchers,　is　proposed　in　this　paper.　RL-CoPref　takes　diverse　program　context　information　as　the　input,　learns　to　maximize　cumulative　rewards,　and　evaluates　prefetch　quality　based　on　prefetch　hits/misses　and　memory　bandwidth　utilization.　It　can　dynamically　adjust　the　prefetch　activation　and　prefetch　degree,　enabling　multiple　prefetchers　to　complement　each　other　on　mixed　applications.　Our　extensive　evaluation,　utilizing　the　ChampSim　simulator,　demonstrates　that　RL-CoPref　can　effectively　adapt　to　various　workloads　and　system　configurations,　optimizing　prefetch　control.　On　average,　RL-CoPref　achieves　76.15%　prefetch　coverage,　having　35.50%　IPC　improvement,　outperforming　state-of-the-art　individual　prefetchers　by　5.91–16.54%　and　outperforming　SBP,　a　state-of-the-art　(non-RL)　prefetch　controller,　by　4.64%.　©　The　Author(s)　2024.