LAUSR

Most safe reinforcement learning (RL) algorithms depend on the accurate reward that is rarely available in wireless security applications and suffer from severe performance degradation for the learning agents that have to choose the policy from a large action set. In this paper, we propose a safe RL algorithm, which uses a policy priority-based hierarchical structure to divide each policy into sub-policies with different selection priorities and thus compresses the action set. By applying inter-agent transfer learning to initialize the learning parameters, this algorithm accelerates the initial exploration of the optimal policy. Based on a security criterion that evaluates the risk value, the sub-policy distribution formulation avoids the dangerous sub-policies that cause learning failure such as severe network security problems in wireless security applications, e.g., Internet services interruption. We also propose a deep safe RL and design four deep neural networks in each sub-policy selection to further improve the learning efficiency for the learning agents that support four convolutional neural networks (CNNs): The Q-network evaluates the long-term expected reward of each sub-policy under the current state, and the E-network evaluates the long-term risk value. The target Q and E-networks update the learning parameters of the corresponding CNN to improve the policy exploration stability. As a case study, our proposed safe RL algorithms are implemented in the anti-jamming communication of unmanned aerial vehicles (UAVs) to select the frequency channel and transmit power to the ground node. Experimental results show that our proposed schemes significantly improve the UAV communication performance, save the UAV energy and increase the reward compared with the benchmark against jamming.