Search Results for author:
Found 7 papers, 2 papers with code
Novelty Detection in Reinforcement Learning with World Models
We refer to the sudden change in visual properties or state transitions as novelties.
Neuro-Symbolic World Models for Adapting to Open World Novelty
Additionally, WorldCloner augments the policy learning process using imagination-based adaptation, where the world model simulates transitions of the post-novelty environment to help the policy adapt.
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty
We provide an ontology of for novelties most relevant to sequential decision making, which distinguishes between novelties that affect objects versus actions, unary properties versus non-unary relations, and the distribution of solutions to a task.
Automated Story Generation as Question-Answering
Neural language model-based approaches to automated story generation suffer from two important limitations.
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning
Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time.
Ranked #5 on
Class Incremental Learning
on cifar100
Fabula Entropy Indexing: Objective Measures of Story Coherence
Automated story generation remains a difficult area of research because it lacks strong objective measures.
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer
Our work investigates whether we can significantly reduce this memory budget by leveraging unlabeled data from an agent's environment in a realistic and challenging continual learning paradigm.
