By　migrating　tasks　from　the　end　devices　to　the　edge　or　cloud,　cooperative　computing　in　the　Internet　of　Things　can　support　time-sensitive,　high-dimensional,　and　complex　applications　while　utilizing　existing　resources,　such　as　the　network　bandwidth,　computing　resources,　and　storage　capacity.　How　to　design　the　multiresource　allocation　system　efficiently　is　a　significant　research　problem.　In　this　article,　we　design　a　multiresource　allocation　system　for　cooperative　computing　in　the　Internet　of　Things　based　on　deep　reinforcement　learning　by　redefining　latency　calculation　models　for　communication,　computation,　and　caching　with　the　consideration　of　practical　interference　factors,　such　as　the　Gaussian　noise　and　data　loss.　The　proposed　system　uses　actor-critic　as　the　base　model　for　rapidly　approximating　the　optimal　policy　by　updating　parameters　of　the　actor　and　critic　in　respective　gradient　directions.　The　balance　control　parameter　is　introduced　to　fit　the　desired　learning　rate　and　actual　learning　rate.　At　the　same　time,　we　use　the　method　of　double　experience　pool　to　limit　the　exploration　direction　of　the　optimal　policy,　which　reduces　the　time　complexity　and　space　complexity　of　the　problem　solution　and　improves　the　adaptability　and　reliability　of　the　scheme.　Experiments　have　demonstrated　that　multiresource　allocation　algorithm　based　on　deep　reinforcement　learning　(DRL-MRA)　performs　well　in　terms　of　the　average　service　latency　under　resource-constrained　conditions,　and　the　improvement　is　significant　with　the　increase　of　network　size.