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This paper presents a novel deep reinforcement learning-based resourceallocation technique for the multi-agent environment presented by a cognitiveradio network that coexists through underlay dynamic spectrum access (DSA) witha primary network. The resource allocation technique presented in this work isdistributed, not requiring coordination with other agents. The presentedalgorithm is the first deep reinforcement learning technique for whichconvergence to equilibrium policies can be shown in the non-stationarymulti-agent environment that results from the uncoordinated dynamic interactionbetween radios through the shared wireless environment. Moreover, simulationresults show that in a finite learning time the presented technique is able tofind policies that yield performance within 3 % of an exhaustive searchsolution, finding the optimal policy in nearly 70 % of cases. Moreover, it isshown that standard single-agent deep reinforcement learning may not achieveconvergence when used in a non-coordinated, coupled multi-radio scenario.

Deep Reinforcement Learning for Distributed Uncoordinated Cognitive Radios Resource Allocation