Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
Authors: Guan-Ting Liu, En-Pei Hu, Pu-Jen Cheng, Hung-Yi Lee, Shao-Hua Sun
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design and conduct experiments to compare our proposed framework (HPRL) to its variants and baselines. [...] The experimental results on Table 1 show that HPRL-PPO outperforms all other approaches on all tasks. |
| Researcher Affiliation | Academia | 1National Taiwan University, Taipei, Taiwan. Correspondence to: Shao-Hua Sun <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 HPRL: Learning Latent Program Embedding Space [...] Algorithm 2 HPRL: Meta-Policy Training |
| Open Source Code | No | Project page: https://nturobotlearninglab.github.io/hprl |
| Open Datasets | No | The Karel program dataset used in this work includes one million programs. All the programs are generated based on syntax rules of the Karel DSL with a maximum length of 40 program tokens. |
| Dataset Splits | Yes | The Karel program dataset used in this work includes 1 million program sequences, with 85% as the training dataset and 15% as the evaluation dataset. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, etc.) were provided for running experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., 'PyTorch 1.9') were provided. |
| Experiment Setup | Yes | Table 9. Hyperparameters of VAE Pretraining [...] Table 10. Hyperparameters of HPRL-PPO and HPRL-SAC Training |