BackgroundThe　rapid　evolution　of　Internet　of　Things　(IoT)　technologies　has　driven　innovations　across　domains　such　as　robotics,　autonomous　systems,　and　environmental　control.　However,　effectively　learning　graph-based　representations　within　these　dynamic　and　heterogeneous　systems　remains　a　significant　challenge,　especially　when　scalability　and　adaptability　are　required.AimsThis　study　aims　to　develop　and　evaluate　a　novel　meta-reinforcement　learning　(meta-RL)　framework　that　combines　Deep　Q-Networks　(DQNs)　with　Graph　Convolutional　Networks　(GCNs)　to　learn　adaptive　and　efficient　representations　of　graph　clusters.　The　primary　objective　is　to　enhance　cluster-based　representation　learning　by　integrating　reinforcement　learning　with　graph　aggregation　policies.MethodsWe　propose　a　cluster　policy-GNN　model　that　formulates　optimal　graph　aggregation　as　a　Markov　Decision　Process　(MDP).　The　framework　incorporates　a　cluster　meta-policy　to　guide　node-specific　aggregation　strategies　and　utilizes　a　combination　of　DQN　and　GCN　for　adaptive　graph　representation.　Training　involves　clustering　nodes　based　on　policy-determined　hops　and　batching　to　ensure　efficient　GNN　training.　A　custom　reward　function　drives　the　reinforcement　learning　process　to　prioritize　computational　focus　on　the　most　informative　subgraphs.ResultsOur　experimental　results,　benchmarked　on　real-world　graph　datasets,　demonstrate　that　the　proposed　framework　significantly　outperforms　existing　state-of-the-art　methods,　including　static　GNNs,　alternating　graph-regularized　networks,　and　causal-aware　neural　architecture　search　models.　The　learned　cluster　policies　effectively　enhance　representation　learning　by　dynamically　adjusting　to　the　structural　heterogeneity　of　input　graphs.　Improvements　were　observed　across　various　domains　and　scales,　validating　the　flexibility　and　generalizability　of　the　method.ConclusionThe　proposed　meta-RL　framework　with　integrated　DQN　and　GCN　modules　offers　a　powerful　and　scalable　approach　for　graph　cluster　representation　learning.　By　introducing　adaptive,　node-specific　aggregation　strategies　guided　by　reinforcement　learning,　the　method　effectively　captures　complex　graph　structures　and　surpasses　current　techniques.　Future　work　may　explore　real-time　adaptation　and　deployment　in　more　dynamic　IoT-based　applications.