Sim-to-real transfer learning techniques can significantly enhance the sample efficiency of the Continual Reinforcement Learning (CRL) framework in wireless network optimization scenarios by bridging the gap between simulated environments and real-world deployment. Here are some ways to leverage sim-to-real transfer learning:
Domain Adaptation: Train the CRL model in simulation environments that closely mimic real-world wireless networks. By fine-tuning the model on limited real data, the learned policies can be adapted to the specific characteristics of the deployment environment.
Imitation Learning: Use imitation learning to transfer policies learned in simulation to real-world settings. By mimicking expert behavior or pre-trained policies, the CRL framework can bootstrap its learning process in the actual deployment scenario.
Policy Distillation: Employ policy distillation techniques to transfer knowledge from a complex simulation environment to a simpler real-world setting. By distilling the learned policies into more compact and efficient representations, the CRL framework can achieve better generalization and sample efficiency.
By integrating sim-to-real transfer learning techniques into the CRL framework, wireless network optimization can benefit from improved performance, reduced data requirements, and faster deployment lead times.