Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate this system and analyze the behavior of this agent, we designed and released our own reinforcement learning agent environment, "the Room", where an agent has to learn how to encode, store, and retrieve memories to maximize its return by answering questions. We show that our deep Q-learning based agent successfully learns whether a short-term memory should be forgotten, or rather be stored in the episodic or semantic memory systems. Our experiments indicate that an agent with human-like memory systems can outperform an agent without this memory structure in the environment.

翻译：在明确的人类记忆系统的认知科学理论的启发下,我们模拟了一个具有短期、偶发和语义记忆系统的代理物,其中每种系统都以知识图为模型。为了评估这个系统和分析这个代理物的行为,我们设计并释放出我们自己的强化学习代理物环境“房间 ”, 在那里,一个代理物必须学会如何编码、储存和检索记忆,以便通过回答问题来最大限度地恢复记忆。我们显示,我们深层次的基于Q学习的代理物成功地学会了短期记忆是应该被遗忘,还是应该被存储在非典型或语义记忆系统中。我们的实验表明,一个具有类似人类的记忆系统的代理物可以在环境中不使用这种记忆结构的情况下超越一个代理物。