Reinforcement learning means learning a policy--a mapping of observations into actions--based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. We present an application of gradient ascent algorithm for reinforcement learning to a complex domain of packet routing in network communication and compare the performance of this algorithm to other routing methods on a benchmark problem.
Applying reinforcement learning to communication often involves optimizing performance with respect to multiple criteria. For a recent discussion on this challenging issue see Shelton @cite_11 . In the context of wireless communication it was addressed by Brown @cite_17 who considers the problem of finding a power management policy that simultaneously maximizes the revenue earned by providing communication while minimizing battery usage. The problem is defined as a stochastic shortest path with discounted infinite horizon, where discount factor varies to model power loss. This approach resulted in significant ( @math 100 @math 6$ computers.
@cite_17 @cite_11
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This paper examines the application of reinforcement learning to a wireless communication problem. The problem requires that channel utility be maximized while simultaneously minimizing battery usage. We present a solution to this multi-criteria problem that is able to significantly reduce power consumption. The solution uses a variable discount factor to capture the effects of battery usage. This paper considers a two-user Gaussian interference channel with energy harvesting transmitters. Different than conventional battery powered wireless nodes, energy harvesting transmitters have to adapt transmission to availability of energy at a particular instant. In this setting, the optimal power allocation problem to maximize the sum throughput with a given deadline is formulated. The convergence of the proposed iterative coordinate descent method for the problem is proved and the short-term throughput maximizing offline power allocation policy is found. Examples for interference regions with known sum capacities are given with directional waterfilling interpretations. Next, stochastic data arrivals are addressed. Finally, online and or distributed near-optimal policies are proposed. Performance of the proposed algorithms are demonstrated through simulations. In this paper, a reinforcement learning-based throughput on demand (ToD) provisioning dynamic power management method (RLTDPM) is proposed for sustaining perpetual operation and satisfying the ToD requirements for today's energy harvesting wireless sensor node (EHWSN). The RLTDPM monitors the environmental state of the EHWS and adjusts their operational duty cycle under criteria of energy neutrality to meet the demanded throughput. Outcomes of these observation-adjustment interactions are then evaluated by feedback reward that represents how well the ToD requests are met; subsequently, the observation-adjustment-evaluation process, so-called reinforcement learning, continues. After the learning process, the RLTDPM is able to autonomously adjust the duty cycle for satisfying the ToD requirement, and in doing so, sustain the perpetual operation of the EHWSN. Simulations of the proposed RLTDPM on a wireless sensor node powered by a battery and solar cell for image sensing tasks were performed. Experimental results demonstrate that the achieved demanded throughput is improved 10.7 for the most stringent ToD requirement, while the residual battery energy of the RLTDPM is improved 7.4 compared with an existing DPM algorithm for EHWSN with image sensing purpose. Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.