3 citations · 5 across the 5 of their papers we have counts for
7 papers · 1 filter
What can we Learn Even From the Weakest? Learning Sketches for Programmatic Strategies
Leandro C. Medeiros, David S. Aleixo, Levi H. S. Lelis
In this paper we show that behavioral cloning can be used to learn effective sketches of programmatic strategies. We show that even the sketches learned by cloning the behavior of…
Policy-Guided Heuristic Search with Guarantees
Laurent Orseau, Levi H. S. Lelis
The use of a policy and a heuristic function for guiding search can be quite effective in adversarial problems, as demonstrated by AlphaGo and its successors, which are based on th…
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces
Zaheen Farraz Ahmad, Levi H. S. Lelis, Michael Bowling
Sample-based planning is a powerful family of algorithms for generating intelligent behavior from a model of the environment. Generating good candidate actions is critical to the s…
Procedural Generation of Initial States of Sokoban
Dâmaris S. Bento, André G. Pereira, Levi H. S. Lelis
Procedural generation of initial states of state-space search problems have applications in human and machine learning as well as in the evaluation of planning systems. In this pap…
Zooming Cautiously: Linear-Memory Heuristic Search With Node Expansion Guarantees
Laurent Orseau, Levi H. S. Lelis, Tor Lattimore
We introduce and analyze two parameter-free linear-memory tree search algorithms. Under mild assumptions we prove our algorithms are guaranteed to perform only a logarithmic factor…
Single-Agent Policy Tree Search With Guarantees
Laurent Orseau, Levi H. S. Lelis, Tor Lattimore +1
We introduce two novel tree search algorithms that use a policy to guide search. The first algorithm is a best-first enumeration that uses a cost function that allows us to prove a…