To extend the proposed fractional multi-agent reinforcement learning (RL) framework for handling complex system dynamics, such as device mobility and heterogeneous task characteristics, several strategies can be employed:
Incorporating Mobility Models: The framework can integrate mobility models that account for the movement patterns of mobile devices. This could involve using stochastic models to predict the trajectory of devices, allowing the RL agents to adapt their scheduling and offloading decisions based on the anticipated changes in device locations and connectivity.
Dynamic State Representation: The state representation in the RL framework can be enhanced to include mobility-related features, such as the current location, speed, and direction of devices. This would enable the agents to make more informed decisions that consider the impact of mobility on task offloading and processing times.
Heterogeneous Task Characteristics: To address heterogeneous task characteristics, the framework can be modified to include task attributes such as size, complexity, and priority. By incorporating these factors into the state and action spaces, the RL agents can optimize their scheduling policies based on the specific requirements of each task, leading to more efficient resource utilization.
Adaptive Learning Rates: The learning rates in the RL algorithms can be made adaptive, allowing the agents to adjust their learning based on the variability in task characteristics and mobility patterns. This would help the agents converge more effectively in dynamic environments.
Multi-Objective Optimization: The framework can be extended to support multi-objective optimization, where agents balance multiple performance metrics, such as minimizing Age of Information (AoI), energy consumption, and task completion time. This would provide a more holistic approach to task scheduling in complex environments.
Simulation and Real-World Testing: Finally, extensive simulation studies and real-world testing can be conducted to validate the extended framework under various scenarios, ensuring that it can effectively handle the complexities introduced by device mobility and heterogeneous tasks.