Deep Reinforcement LearningBased Channel Allocation for Wireless LANs with Graph Convolutional Networks. Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learningbased channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a wellknown method double deep Qnetwork. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theorybased method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods.

GANpowered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demandaware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial networkpowered deep distributional Q network (GANDDQN) to learn the actionvalue distribution driven by minimizing the discrepancy between the estimated actionvalue distribution and the target actionvalue distribution. We put forward a rewardclipping mechanism to stabilize GANDDQN training against the effects of widelyspanning utility values. Moreover, we further develop Dueling GANDDQN, which uses a specially designed dueling generator, to learn the actionvalue distribution by estimating the statevalue distribution and the action advantage function. Finally, we verify the performance of the proposed GANDDQN and Dueling GANDDQN algorithms through extensive simulations.