## 1 Introduction  
The utilization of computation-intensive applications, such as augmented reality (AR), virtual reality (VR), gaming on the internet, smart transportation, industrial and residential automation, developed social networking, and facial recognition, has experienced a surge in popularity due to the widespread adoption of the internet of things (IoT) [1, 2]. It is anticipated that this pattern will persist, given the predicted increase in the quantity of internet of things (IoT) devices, which is estimated to reach 24 billion by the year 2020 [3], and the projected surpassing of 30.6 exabytes per month in worldwide mobile network traffic. These numerous IoT entities, such as mobile users and sensors, will generate an unparalleled amount of data that need processing and analysis for the purpose of delivering the service. Undoubtedly, this massive amount of data represents enormous business opportunities. However, to process these unprecedented amounts of data and to satisfy the computation-intensive applications, the system will consume a tremendous amount of computational energy resources and delays will be generated [1; 3]. Therefore, to tackle computationally costly applications and minimize energy, mobile edge computing (MEC) has been introduced as a groundbreaking architecture embedded with cache and processing capacity in the vicinity of the user equipment (UE) [4; 5; 6]. Edge computing is considered a compliment to cloud computing technologies. It provides significant storage, computing, communication, and management capabilities at the edge. Multi-access edge computing has a remarkable capability to perform collaborative radio signal processing (CRSP) and collaborative radio resource management (CRRM) in real time for user equipment (UE) that is located nearby. On the other hand, C-RAN utilizes centralized signal processing and resource allocation to meet user demand efficiently [5]. Therefore, the MEC network concept has great potential in meeting diverse demands, especially in artificial intelligence (AI)-based future wireless networks [7]. Offloading efficient computations is a promising strategy to reduce network energy consumption and latency. In the context of computation offloading demand of IoT devices in MEC networks, edge computing can play a significant role by executing the computing tasks at the edge instead of sending the data to the remotely located cloud computing tier [8]. However, the computation offloading becomes immensely challenging due to the limitation of embedded computing resources of the edge access point (E-AP). Moreover, the computation offloading problem becomes intractable due to its stochastic nature. It is infeasible to tackle this problem with the old-fashioned algorithm, which takes a long time, unavoidably generates a delay, and consumes more energy [9]. In the past few years, the DRL (deep reinforcement learning) algorithm, a form of single-agent artificial intelligence, has garnered significant interest from the academic and industrial sectors. This is primarily due to its exceptional ability to control and solve highly complex problems [10; 11]. In addition, the DRL method has been widely used for intelligent decisions on computational offloading and allocating resources in IoT networks. However, next-generation IoT networks are more decentralized, and the controller can make the decision in a decentralized manner to enhance the throughput while minimizing energy in these future wireless communication networks [12; 13]. In this article, we investigate a computational offloading and resource allocation technique based on the asynchronous advantage actor-critic (A3C) algorithm. Each E-AP decides the suitable location for offloading the computational tasks in a distributed manner without the knowledge of global information of the network other than the surrounding E-APs.