  In the rapidly evolving landscape of 5G and beyond, cloud-native Open Radio
Access Networks (O-RAN) present a paradigm shift towards intelligent, flexible,
and sustainable network operations. This study addresses the intricate
challenge of energy efficient (EE) resource allocation that services both
enhanced Mobile Broadband (eMBB) and ultra-reliable low-latency communications
(URLLC) users. We propose a novel distributed learning framework leveraging
on-policy and off-policy transfer learning strategies within a deep
reinforcement learning (DRL)--based model to facilitate online resource
allocation decisions under different channel conditions. The simulation results
explain the efficacy of the proposed method, which rapidly adapts to dynamic
network states, thereby achieving a green resource allocation.
