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..
A Laplacian Framework for Option Discovery in Reinforcement Learning
Authors: Marlos C. Machado, Marc G. Bellemare, Michael Bowling
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Empirical Evaluation We used three MDPs in our empirical study (c.f. Figure 1): an open room, an I-Maze, and the 4-room domain. and In these experiments, the agent starts at the bottom left corner and its goal is to reach the top right corner. The agent observes a reward of 0 until the goal is reached, when it observes a reward of +1. We used Q-Learning (alpha = 0.1, gamma = 0.9) to learn a policy over primitive actions. |
| Researcher Affiliation | Collaboration | 1University of Alberta 2Google Deep Mind. |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided; methods are described in prose. |
| Open Source Code | Yes | 1Python code can be found at: https://github.com/mcmachado/options |
| Open Datasets | Yes | We tested our method in the ALE (Bellemare et al., 2013). |
| Dataset Splits | No | The paper does not explicitly state specific dataset splits for training, validation, and testing, such as percentages or counts. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | While Python code is mentioned as available, specific software dependencies like library names with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x) are not provided. |
| Experiment Setup | Yes | We used Q-Learning (alpha = 0.1, gamma = 0.9) to learn a policy over primitive actions. and Episodes were 100 time steps long, and we learned for 250 episodes in the 10x10 grid and in the I-Maze, and for 500 episodes in the 4-room domain. |