4 papers · 1 filter
Gradient-Based Program Synthesis with Neurally Interpreted Languages
Matthew V. Macfarlane, Clément Bonnet, Herke van Hoof +1
A central challenge in program induction has long been the trade-off between symbolic and neural approaches. Symbolic methods offer compositional generalisation and data efficiency…
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…
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…
Searching for Programmatic Policies in Semantic Spaces
Rubens O. Moraes, Levi H. S. Lelis
Syntax-guided synthesis is commonly used to generate programs encoding policies. In this approach, the set of programs, that can be written in a domain-specific language defines th…