Experience replay memory in reinforcement learning enables agents to remember and reuse past experiences. Most of the reinforcement models are subject to single experience replay memory to operate agents. In this article, we propose a framework that accommodates doubly used experience replay memory, exploiting both important transitions and new transitions simultaneously. In numerical studies, the deep -networks (DQN) equipped with double experience replay memory are examined under various scenarios. A self-driving car requires an automated agent to figure out when to adequately change lanes on the real-time basis. To this end, we apply our proposed agent to the simulation of urban mobility (SUMO) experiments. Besides, we also verify its applicability to reinforcement learning whose action space is discrete (e.g., computer game environments). Taken all together, we conclude that the proposed framework outperforms priorly known reinforcement learning models in the virtue of double experience replay memory.

中文翻译：

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双重回放记忆指导的强化学习

强化学习中的经验重播记忆使特工能够记住并重用过去的经验。大多数增强模型都受单一经验重播记忆的约束，以操作代理。在本文中，我们提出了一个框架，该框架可容纳双重使用的体验重播内存，同时利用重要的过渡和新的过渡。在数值研究，深-在各种情况下，都会对配备有双重体验重播内存的网络（DQN）进行检查。自动驾驶汽车需要自动代理实时确定何时适当改变车道。为此，我们将我们提出的代理应用于城市交通模拟（SUMO）实验。此外，我们还验证了其适用于动作空间是离散的强化学习（例如，计算机游戏环境）的适用性。综上所述，我们得出的结论是，借助双重体验重播记忆，所提出的框架优于先前已知的强化学习模型。