What can we Learn Even From the Weakest? Learning Sketches for Programmatic Strategies

22 March 2022

Papers citing "What can we Learn Even From the Weakest? Learning Sketches for Programmatic Strategies"

10 / 10 papers shown

Title
InnateCoder: Learning Programmatic Options with Foundation Models Rubens O. Moraes Quazi Asif Sadmine Hendrik Baier Levi H. S. Lelis OffRL 7 0 0 18 May 2025
Reclaiming the Source of Programmatic Policies: Programmatic versus Latent Spaces Tales H. Carvalho Kenneth Tjhia Levi H. S. Lelis 42 7 0 16 Oct 2024
KnowPC: Knowledge-Driven Programmatic Reinforcement Learning for Zero-shot Coordination Yin Gu Qi Liu Zhi Li Kai Zhang 36 0 0 08 Aug 2024
Searching for Programmatic Policies in Semantic Spaces Rubens O. Moraes Levi H. S. Lelis 40 4 0 08 May 2024
Assessing the Interpretability of Programmatic Policies with Large Language Models Zahra Bashir Michael Bowling Levi H. S. Lelis ELM 21 3 0 12 Nov 2023
Synthesizing Programmatic Policies with Actor-Critic Algorithms and ReLU Networks S. Orfanos Levi H. S. Lelis 27 6 0 04 Aug 2023
Reinforcement Learning and Data-Generation for Syntax-Guided Synthesis Julian Parsert Elizabeth Polgreen 33 3 0 13 Jul 2023
Can You Improve My Code? Optimizing Programs with Local Search Fatemeh Abdollahi Saqib Ameen Matthew E. Taylor Levi H. S. Lelis 22 0 0 10 Jul 2023
Choosing Well Your Opponents: How to Guide the Synthesis of Programmatic Strategies Rubens O. Moraes David S. Aleixo Lucas N. Ferreira Levi H. S. Lelis 16 7 0 10 Jul 2023
Programmatic Imitation Learning from Unlabeled and Noisy Demonstrations Jimmy Xin Linus Zheng Kia Rahmani Jiayi Wei Jarrett Holtz Işıl Dillig Joydeep Biswas 30 1 0 02 Mar 2023