  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.
