Indirect Reinforcement Learning for Autonomous Power Configuration and Control in Wireless Networks

In this paper, non deterministic Indirect Reinforcement Learning (RL) techniques for controlling the transmission times and power of Wireless Network nodes are presented. Indirect RL facilitates planning and learning which ultimately leads to convergence on optimal actions with reduced episodes or time steps compared to direct RL. Three Dyna architecture based algorithms for non deterministic environments are presented. The results show improvements over direct RL and conventional static power control techniques.