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Policy gradient reinforcement learning techniques enable an agent to directlylearn an optimal action policy through the interactions with the environment.Nevertheless, despite its advantages, it sometimes suffers from slowconvergence speed. Inspired by human decision making approach, we work towardenhancing its convergence speed by augmenting the agent to memorize and use therecently learned policies. We apply our method to the trust-region policyoptimization (TRPO), primarily developed for locomotion tasks, and proposefaded-experience (FE) TRPO. To substantiate its effectiveness, we adopt it tolearn continuous power control in an interference channel when only noisylocation information of devices is available. Results indicate that withFE-TRPO it is possible to almost double the learning speed compared to TRPO.Importantly, our method neither increases the learning complexity nor imposesperformance loss.

Faded-Experience Trust Region Policy Optimization for Model-Free Power Allocation in Interference Channel