  5G beyond is an end-edge-cloud orchestrated network that can exploit
heterogeneous capabilities of the end devices, edge servers, and the cloud and
thus has the potential to enable computation-intensive and delay-sensitive
applications via computation offloading. However, in multi user wireless
networks, diverse application requirements and the possibility of various radio
access modes for communication among devices make it challenging to design an
optimal computation offloading scheme. In addition, having access to complete
network information that includes variables such as wireless channel state, and
available bandwidth and computation resources, is a major issue. Deep
Reinforcement Learning (DRL) is an emerging technique to address such an issue
with limited and less accurate network information. In this paper, we utilize
DRL to design an optimal computation offloading and resource allocation
strategy for minimizing system energy consumption. We first present a
multi-user end-edge-cloud orchestrated network where all devices and base
stations have computation capabilities. Then, we formulate the joint
computation offloading and resource allocation problem as a Markov Decision
Process (MDP) and propose a new DRL algorithm to minimize system energy
consumption. Numerical results based on a real-world dataset demonstrate that
the proposed DRL-based algorithm significantly outperforms the benchmark
policies in terms of system energy consumption. Extensive simulations show that
learning rate, discount factor, and number of devices have considerable
influence on the performance of the proposed algorithm.
