Fast Reinforcement Learning for Anti-jamming Communications
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This letter presents a fast reinforcement learning algorithm for anti-jamming communications which chooses previous action with probability τ and applies ϵ-greedy with probability (1-τ). A dynamic threshold based on the average value of previous several actions is designed and probability τ is formulated as a Gaussian-like function to guide the wireless devices. As a concrete example, the proposed algorithm is implemented in a wireless communication system against multiple jammers. Experimental results demonstrate that the proposed algorithm exceeds Q-learing, deep Q-networks (DQN), double DQN (DDQN), and prioritized experience reply based DDQN (PDDQN), in terms of signal-to-interference-plus-noise ratio and convergence rate.
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