This paper examines the application of reinforcement learning to a wire(cid:173) less communication problem. The problem requires that channel util(cid:173) ity be maximized while simultaneously minimizing battery usage. We present a solution to this multi-criteria problem that is able to signifi(cid:173) cantly reduce power consumption. The solution uses a variable discount factor to capture the effects of battery usage.

Papers published at the Neural Information Processing Systems Conference.

This paper examines the application of reinforcement learning to a wireless communicationproblem. 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 reducepower consumption. The solution uses a variable discount factor to capture the effects of battery usage. 1 Introduction Reinforcement learning (RL) has been applied to resource allocation problems in telecommunications, e.g.,channel allocation in wireless systems, network routing, and admission control in telecommunication networks [1,2, 8, 10]. These have demonstrated reinforcement learningcan find good policies that significantly increase the application reward within the dynamics of the telecommunication problems.