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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…

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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 26Source ↗PDF ↗Scholar ↗