  The next-generation wireless networks are required to satisfy a variety of
services and criteria concurrently. To address upcoming strict criteria, a new
open radio access network (O-RAN) with distinguishing features such as flexible
design, disaggregated virtual and programmable components, and intelligent
closed-loop control was developed. O-RAN slicing is being investigated as a
critical strategy for ensuring network quality of service (QoS) in the face of
changing circumstances. However, distinct network slices must be dynamically
controlled to avoid service level agreement (SLA) variation caused by rapid
changes in the environment. Therefore, this paper introduces a novel framework
able to manage the network slices through provisioned resources intelligently.
Due to diverse heterogeneous environments, intelligent machine learning
approaches require sufficient exploration to handle the harshest situations in
a wireless network and accelerate convergence. To solve this problem, a new
solution is proposed based on evolutionary-based deep reinforcement learning
(EDRL) to accelerate and optimize the slice management learning process in the
radio access network's (RAN) intelligent controller (RIC) modules. To this end,
the O-RAN slicing is represented as a Markov decision process (MDP) which is
then solved optimally for resource allocation to meet service demand using the
EDRL approach. In terms of reaching service demands, simulation results show
that the proposed approach outperforms the DRL baseline by 62.2%.
