The　rapid　development　of　the　Industrial　Internet　of　Things　(IIoT)　enables　IIoT　devices　to　offload　their　computation-intensive　tasks　to　nearby　edges　via　wireless　base　stations　and　thus　relieve　their　resource　constraints.　To　better　guarantee　quality-of-service,　it　has　become　necessary　to　cooperate　multiple　edges　instead　of　letting　them　work　alone.　However,　the　existing　solutions　commonly　use　a　centralized　decision-making　manner　and　cannot　effectively　achieve　good　load　balancing　among　massive　edges　that　are　widely　distributed　in　IIoT　environments.　This　results　in　long　decision-making　time　and　high　communication　costs.　To　address　this　important　problem,　in　this　article,　we　propose　a　reinforcement　learning　(RL)-empowered　feedback　control　method　for　cooperative　load　balancing　(RF-CLB).　First,　by　integrating　RL　and　machine　learning　(ML)　algorithms,　each　edge　independently　schedules　tasks　and　performs　load　balancing　between　adjacent　edges　based　on　the　local　information.　Next,　through　feedback　control　and　multiedge　cooperation,　the　objective　multiedge　load-balancing　plan　for　IIoT　can　be　found.　Simulation　results　demonstrate　that　the　RF-CLB　chooses　the　adjustment　operations　of　load　balancing　with　96.3%　correctness.　Moreover,　the　RF-CLB　achieves　the　near-optimal　performance,　which　outperforms　the　classic　ML-based　and　rule-based　methods　by　6-9%　and　10-12%,　respectively.