Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation
This addresses resource allocation for cognitive radios in distributed settings, offering incremental improvements in learning efficiency and convergence.
The paper tackles resource allocation in cognitive radio networks using a distributed deep reinforcement learning approach that converges to equilibrium policies in non-stationary multi-agent environments, achieving optimal policies in 99% of cases and requiring less than half the learning steps compared to table-based Q-learning.
This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network where the interactions of the agents during learning may lead to a non-stationary environment. The resource allocation technique presented in this work is distributed, not requiring coordination with other agents. It is shown by considering aspects specific to deep reinforcement learning that the presented algorithm converges in an arbitrarily long time to equilibrium policies in a non-stationary multi-agent environment that results from the uncoordinated dynamic interaction between radios through the shared wireless environment. Simulation results show that the presented technique achieves a faster learning performance compared to an equivalent table-based Q-learning algorithm and is able to find the optimal policy in 99% of cases for a sufficiently long learning time. In addition, simulations show that our DQL approach requires less than half the number of learning steps to achieve the same performance as an equivalent table-based implementation. Moreover, it is shown that the use of a standard single-agent deep reinforcement learning approach may not achieve convergence when used in an uncoordinated interacting multi-radio scenario