  AI heralds a step-change in the performance and capability of wireless
networks and other critical infrastructures. However, it may also cause
irreversible environmental damage due to their high energy consumption. Here,
we address this challenge in the context of 5G and beyond, where there is a
complexity explosion in radio resource management (RRM). On the one hand, deep
reinforcement learning (DRL) provides a powerful tool for scalable optimization
for high dimensional RRM problems in a dynamic environment. On the other hand,
DRL algorithms consume a high amount of energy over time and risk compromising
progress made in green radio research. This paper reviews and analyzes how to
achieve green DRL for RRM via both architecture and algorithm innovations.
Architecturally, a cloud based training and distributed decision-making DRL
scheme is proposed, where RRM entities can make lightweight deep local
decisions whilst assisted by on-cloud training and updating. On the algorithm
level, compression approaches are introduced for both deep neural networks and
the underlying Markov Decision Processes, enabling accurate low-dimensional
representations of challenges. To scale learning across geographic areas, a
spatial transfer learning scheme is proposed to further promote the learning
efficiency of distributed DRL entities by exploiting the traffic demand
correlations. Together, our proposed architecture and algorithms provide a
vision for green and on-demand DRL capability.
