  This paper addresses how to handle slice resources for 6G networks at
different time scales in an architecture based on an open radio access network
(ORAN). The proposed solution includes artificial intelligence (AI) at the edge
of the network and applies two control-level loops to obtain optimal
performance compared to other techniques. The ORAN facilitates programmable
network architectures to support such multi-time scale management using AI
approaches. The proposed algorithms analyze the maximum utilization of
resources from slice performance to take decisions at the inter-slice level.
Inter-slice intelligent agents work at a non-real-time level to reconfigure
resources within various slices. Further than meeting the slice requirements,
the intra-slice objective must also include the minimization of maximum
resource utilization. This enables smart utilization of the resources within
each slice without affecting slice performance. Here, each xApp that is an
intra-slice agent aims at meeting the optimal quality of service (QoS) of the
users, but at the same time, some inter-slice objectives should be included to
coordinate intra- and inter-slice agents. This is done without penalizing the
main intra-slice objective. All intelligent agents use deep reinforcement
learning (DRL) algorithms to meet their objectives. We have presented results
for enhanced mobile broadband (eMBB), ultra-reliable low latency (URLLC), and
massive machine type communication (mMTC) slice categories.
