Search Results for author:
Found 3 papers, 2 papers with code
Hierarchical Width-Based Planning and Learning
In classical planning, we show how IW(1) at two levels of abstraction can solve problems of width 2.
Deep Policies for Width-Based Planning in Pixel Domains
Surprisingly, we observe that the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner.
Improving width-based planning with compact policies
The planning step hinges on the Iterated-Width (IW) planner, a state of the art planner that makes explicit use of the state representation to perform structured exploration.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
