Search Results for author:
Found 2 papers, 1 papers with code
Proper Laplacian Representation Learning
The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly challenging.
Learning Transferable Concepts in Deep Reinforcement Learning
While humans and animals learn incrementally during their lifetimes and exploit their experience to solve new tasks, standard deep reinforcement learning methods specialize to solve only one task at a time.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
