Papers › A Machine with Short-Term, Episodic, and Semantic Memory Systems
A Machine with Short-Term, Episodic, and Semantic Memory Systems
Taewoon Kim, Michael Cochez, Vincent François-Lavet, Mark Neerincx, Piek Vossen
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.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| RoomEnv-v1 | RoomEnv-v1 | HumemAI-v0-capacity=32-semantic-pretrained | final agent reward | 116.5 | #1 of 2 | Archive leaderboard | report |
| RoomEnv-v1 | RoomEnv-v1 | HumemAI-v0-capacity=32-semantic-scratch | final agent reward | 109 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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