Optimum power allocation is an effective way to mitigate residual self-interference and interuser interference in multiple input multiple output full-duplex (FD) systems. However, current research mainly considers parts of influencing factors and sets service models fixed. Given this, we comprehensively focus on three perspectives in a novel power allocation method, which involve the muting management (MM) and the assignment of both base station antennas and subcarriers in the FD system. Then, we formulate an optimization problem to maximize the total spectrum efficiency. According to the categories of variables in the nonconvex objective function, we first propose a hierarchical algorithm, which is decomposed into the first-order Taylor approximation (FOTA) method and the greedy algorithm. The continuous and discrete variables related subproblems are solved through FOTA and greedy algorithm, respectively. Among them, the greedy algorithm is an alternative to a traditional method of exhaustive search. Considering the high complexity of the greedy algorithm, we further introduce deep reinforcement learning (DRL) instead to solve the corresponding subproblem. Thus, two Double Deep Q-learning Networks are constructed to train the samples in each sub-slot. Simulation results validate that the hybrid DRL-convex method outperforms the hybrid greedy-convex method. Meanwhile, the MM introduced scheme's performance gain is more evident than that of the method without MM in many scenarios.INDEX TERMS full-duplex (FD), power allocation, spectrum efficiency (SE), smart antennas, muting management (MM), subcarrier assignment, deep reinforcement learning (DRL), jamming user (JU).