  Dynamic resource allocation plays a critical role in the next generation of
intelligent wireless communication systems. Machine learning has been leveraged
as a powerful tool to make strides in this domain. In most cases, the progress
has been limited to simulations due to the challenging nature of hardware
deployment of these solutions. In this paper, for the first time, we design and
deploy deep reinforcement learning (DRL)-based power control agents on the GPU
embedded software defined radios (SDRs). To this end, we propose an end-to-end
framework (MR-iNet Gym) where the simulation suite and the embedded SDR
development work cohesively to overcome real-world implementation hurdles. To
prove feasibility, we consider the problem of distributed power control for
code-division multiple access (DS-CDMA)-based LPI/D transceivers. We first
build a DS-CDMA ns3 module that interacts with the OpenAI Gym environment.
Next, we train the power control DRL agents in this ns3-gym simulation
environment in a scenario that replicates our hardware testbed. Next, for edge
(embedded on-device) deployment, the trained models are optimized for real-time
operation without loss of performance. Hardware-based evaluation verifies the
efficiency of DRL agents over traditional distributed constrained power control
(DCPC) algorithm. More significantly, as the primary goal, this is the first
work that has established the feasibility of deploying DRL to provide optimized
distributed resource allocation for next-generation of GPU-embedded radios.
