The　problem　of　shared　node　selection　and　cache　placement　in　wireless　networks　is　challenging　due　to　the　difficulty　of　finding　low-complexity　optimal　solutions.　This　paper　proposes　a　new　approach　combining　Lyapunov　optimization　and　reinforcement　learning　(LoRL)　to　address　content　sharing　in　heterogeneous　mobile　edge　computing　(MEC)　networks　with　base　station　(BS)　and　device-to-device　(D2D)　communication.　Device　in　this　network　can　choose　to　establish　D2D　links　with　neighboring　devices　for　content　sharing　or　send　requests　directly　to　the　base　station　for　content.　Content　access　and　energy　consumption　of　shared　nodes　are　modeled　as　a　queuing　system.　The　goal　is　to　assign　content　sharing　nodes　to　stabilize　all　queues　while　maximizing　D2D　sharing　gain　and　minimizing　latency,　even　in　the　presence　of　unknown　network　state　distribution　and　user　sharing　costs.　The　proposed　approach　enables　edge　device　to　independently　select　associated　nodes　and　make　caching　decisions,　thereby　minimizing　time-averaged　network　costs　and　stabilizing　the　queuing　system.　Experimental　results　show　that　the　proposed　algorithm　converges　to　the　optimal　policy　and　outperforms　other　policies　in　terms　of　total　queue　backlog　trade-off　and　network　cost.