2 citations · 4 across the 4 of their papers we have counts for
4 papers
Self-Improvement for Fast, High-Quality Plan Generation
Robert Gieselmann, Henrike von Huelsen, Mihai Samson +9
Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, rather than a high-quality sol…
Towards a More Complete Theory of Function Preserving Transforms
Michael Painter
In this paper, we develop novel techniques that can be used to alter the architecture of a neural network, while maintaining the function it represents. Such operations are known a…
Monte Carlo Tree Search with Boltzmann Exploration
Michael Painter, Mohamed Baioumy, Nick Hawes +1
Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound applied to Trees (UCT), are instrumental to automated planning techniques. However, UCT can be slow to explor…
Convex Hull Monte-Carlo Tree Search
Michael Painter, Bruno Lacerda, Nick Hawes
This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives. We propose the Convex Hull Monte-Carlo Tree-Search (CHMCTS) framework,…