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Guan-Ting Liu

2 accepted papers

2024

Hierarchical Programmatic Option Framework

NeurIPS 2024poster

Deep reinforcement learning aims to learn deep neural network policies to solve large-scale decision-making problems. However, approximating policies using deep neural networks makes it difficult to interpret the learned decision-making process. To address this issue, prior works (Trivedi et al., 20…

Cited by 1SourcePDFScholar
2023

Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs

ICML 2023poster

Aiming to produce reinforcement learning (RL) policies that are human-interpretable and can generalize better to novel scenarios, Trivedi et al. (2021) present a method (LEAPS) that first learns a program embedding space to continuously parameterize diverse programs from a pre-generated program data…

Cited by 18SourcePDFScholar