  We consider an energy harvesting (EH) transmitter communicating with a
receiver through an EH relay. The harvested energy is used for data
transmission, including the circuit energy consumption. As in practical
scenarios, the system state, comprised by the harvested energy, battery levels,
data buffer levels, and channel gains, is only partially observable by the EH
nodes. Moreover, the EH nodes have only outdated knowledge regarding the
channel gains for their own transmit channels. Our goal is to find distributed
transmission policies aiming at maximizing the throughput. A channel predictor
based on a Kalman filter is implemented in each EH node to estimate the current
channel gain for its own channel. Furthermore, to overcome the partial
observability of the system state, the EH nodes cooperate with each other to
obtain information about their parameters during a signaling phase. We model
the problem as a Markov game and propose a multi-agent reinforcement learning
algorithm to find the transmission policies. We show the trade-off between the
achievable throughput and the signaling required, and provide convergence
guarantees for the proposed algorithm. Results show that even when the
signaling overhead is taken into account, the proposed algorithm outperforms
other approaches that do not consider cooperation.
