6 citations · 9 across the 5 of their papers we have counts for
5 papers
Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
Taewoon Kim, Vincent François-Lavet, Michael Cochez
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 tr…
Temporal Knowledge-Graph Memory in a Partially Observable Environment
Taewoon Kim, Vincent François-Lavet, Michael Cochez
Agents in partially observable environments require persistent memory to integrate observations over time. While KGs (knowledge graphs) provide a natural representation for such ev…
Hadamard Representation: Scaffolding Performance Across Model-free RL
Jacob E. Kooi, Zhao Yang, Mark Hoogendoorn +1
Deep reinforcement learning agents progressively lose representational capacity during training: neurons become dormant, removing active capacity from the network, and effective ra…
A Machine with Short-Term, Episodic, and Semantic Memory Systems
Taewoon Kim, Michael Cochez, Vincent François-Lavet +2
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 mod…
Domain Adversarial Reinforcement Learning
Bonnie Li, Vincent François-Lavet, Thang Doan +1
We consider the problem of generalization in reinforcement learning where visual aspects of the observations might differ, e.g. when there are different backgrounds or change in co…