… As a model-free approach, our RL-based edge computing resource management framework
… proposed RL-based resource management framework as shown in Fig. 1, which consists of …

… resource management and interference mitigation in wireless networks using multi-agent
deep reinforcement learning (RL). We equip each transmitter in the network with a deep RL …

… , a multi-dimensional resource management problem is formulated to … deep reinforcement
learning based resource management scheme is proposed to make real-time optimal resource …

… problem of joint power control and computing resource allocation for MEC in IIoT. In order to
… , we propose a deep reinforcement learningbased dynamic resource management (DDRM) …

… Thus, to measure the performance of the two proposed deep RL-based resource management
schemes, we define delay/QoS satisfaction ratio as the number of vehicles with satisfied …

… This article proposes an innovative resource management … satellite systems and maximizing
resource utilization. The key … -based management offering the matching between resources …

… Deep reinforcement learning (eg, deep Ο-learning, DQL) is assumed to be an appropriate
algorithm to solve the demand-aware interslice resource management issue in network slicing …

… Then, in order to achieve real-time and low overhead computation offloading decisions and
resource allocation strategies, we design a novel two-timescale deep reinforcement learning …

… of information (AoI)-aware radio resource management for expected long-term performance
… long short-term memory and deep reinforcement learning techniques to address the partial …

… The resource allocation problem in the MEC system is formulated as a … A deep reinforcement
learning approach is applied to solve the problem. We also propose an improved deep Q-…