4 papers
Common Benchmarks Undervalue the Generalization Power of Programmatic Policies
Amirhossein Rajabpour, Kiarash Aghakasiri, Sandra Zilles +1
Algorithms for learning programmatic representations for sequential decision-making problems are often evaluated on out-of-distribution (OOD) problems, with the common conclusion t…
Subgoal-Guided Policy Heuristic Search with Learned Subgoals
Jake Tuero, Michael Buro, Levi H. S. Lelis
Policy tree search is a family of tree search algorithms that use a policy to guide the search. These algorithms provide guarantees on the number of expansions required to solve a…
InnateCoder: Learning Programmatic Options with Foundation Models
Rubens O. Moraes, Quazi Asif Sadmine, Hendrik Baier +1
Outside of transfer learning settings, reinforcement learning agents start their learning process from a clean slate. As a result, such agents have to go through a slow process to…
Exponential Speedups by Rerooting Levin Tree Search
Laurent Orseau, Marcus Hutter, Levi H. S. Lelis
Levin Tree Search (LTS) (Orseau et al., 2018) is a search algorithm for deterministic environments that uses a user-specified policy to guide the search. It comes with a formal gua…