Search Results for author:
Found 4 papers, 1 papers with code
Reconciling Spatial and Temporal Abstractions for Goal Representation
In this paper, we propose a novel three-layer HRL algorithm that introduces, at different levels of the hierarchy, both a spatial and a temporal goal abstraction.
Ranked #1 on
Hierarchical Reinforcement Learning
on Ant + Maze
Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis
Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning.
Goal Space Abstraction in Hierarchical Reinforcement Learning via Reachability Analysis
Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning.
DroidStar: Callback Typestates for Android Classes
We use DroidStar to learn callback typestates for Android classes both for cases where one is already provided by the documentation, and for cases where the documentation is unclear.
