MR iNet Gym

MR-iNet Gym is a framework facilitating the deployment and testing of deep reinforcement learning (DRL) algorithms for resource allocation in wireless communication systems, specifically focusing on embedded software-defined radios. Current research emphasizes the use of DRL agents, often trained in simulation environments like ns3-Gym, for optimizing power control and other resource management tasks, with a strong focus on achieving successful sim-to-real transfer. This work is significant because it bridges the gap between simulated DRL research and real-world applications, enabling the development and evaluation of more efficient and adaptable wireless communication systems.