In　this　paper,　we　address　the　problem　of　deep　Q-Network　(DQN)　based　joint　power　control　and　beamforming　for　enabling　interference　suppression　in　software　defined　UAV　swarm　(SD-UAS)　network.　We　first　present　the　optimization　model　of　joint　beamforming　and　power　control　(JBPC).　Then,　to　solve　this　joint　optimization　problem,　we　make　use　of　UCB　exploration　in　the　learning　process　of　DQN.　Simulation　results　validate　that　the　convergence　obtained　with　the　proposed　UCB-DQN　strategy　outperform　the　DQN　learning　algorithm,　which　can　to　raise　the　exploration　efficiency　and　sequentially　speed　up　the　convergence　for　the　JBPC　problem.　It　is　benefit　to　supress　interference　and　enhance　the　SD　-UAS　communication　performance.　©　Published　under　licence　by　IOP　Publishing　Ltd.