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A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios

A data dissemination scenario is considered. The transmitter harvests energy from the environment and uses it to transmit individual data to multiple receivers. We consider a realistic scenario in which only causal knowledge regarding the energy harvesting, the channel fading and the data arrival processes is available. Our goal is to find a power allocation policy aiming at maximizing the throughput. We propose a two-layer reinforcement learning algorithm which divides the learning task into two sub-tasks, namely, how much power to use in each time interval and how to split the power among the data to be transmitted. By dividing the task, we increase the learning speed as compared to the standard reinforcement learning algorithms Q-Iearning and SARSA. Moreover, the proposed algorithm outperforms reference policies that deplete the battery in every time interval.