To extend the Continual Reinforcement Learning (CRL) framework to handle non-overlapping action spaces across tasks, where the action space in the target task is completely different from the source task, several modifications and adaptations can be implemented:
Task-specific Policy Networks: Instead of sharing a policy network across tasks, individual policy networks can be trained for each task. This allows for task-specific architectures tailored to the unique action spaces of each task.
Knowledge Distillation: Knowledge distillation can be employed to transfer knowledge from the source task to the target task, even when the action spaces are non-overlapping. By distilling the learned policies or representations from the source task into the target task's policy network, valuable insights and strategies can be transferred.
Transfer Learning Techniques: Techniques from transfer learning can be utilized to adapt the policy learned in the source task to the target task with a different action space. This can involve fine-tuning the source policy on the target task data or using pre-trained models as initializations for the target task.
Dynamic Action Space Expansion: Implement a mechanism that dynamically expands the action space of the policy network as it transitions from one task to another. This adaptive expansion can accommodate the varying action requirements of different tasks.
By incorporating these strategies, the CRL framework can effectively handle non-overlapping action spaces across tasks, ensuring adaptability and efficiency in optimizing wireless network configurations.