This is the pre-proceedings for the RLC 2026. You may expect minor changes.
Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
Will be presented at the Reinforcement Learning Conference (RLC), Montréal, Quebec, Canada, August 15–17, 2026.
Keywords: neuro-symbolic reinforcement learning, partial observability, knowledge-graph
Reinforcement learning under partial observability requires deciding what information to retain, yet most memory-based approaches do not explicitly model short-term-to-long-term transfer of symbolic observations. We study this transfer process in a temporal knowledge-graph memory setting and cast it as a neuro-symbolic value-based decision problem: for each observed triple, the agent chooses whether to keep or drop it before long-term insertion. To handle variable-sized short-term buffers, we use a per-item Q-learning design with shared parameters and a practical temporal-difference update over matched items across consecutive steps. On the RoomKG benchmark at long-term memory capacity 128, learned transfer decisions outperform symbolic and neural baselines, including symbolic baselines with temporal annotations and history-based LSTM/Transformer baselines. Across transfer-policy ablations, a lightweight local short-term-only variant performs best, and step-level behavior shows that the policy keeps navigation- and query-relevant facts while discarding lower-value candidate facts, supporting explicit and interpretable memory decisions under memory constraints.
Taewoon Kim, Vincent Francois-Lavet, and Michael Cochez. "Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability." Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
BibTeX:@article{kim2026shorttermtolongterm,
title={Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability},
author={Taewoon Kim and Vincent Francois-Lavet and Michael Cochez},
journal={Reinforcement Learning Journal},
volume={7},
pages={},
year={2026}
}