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..
Layered State Discovery for Incremental Autonomous Exploration
Authors: Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical Evaluations We implemented our LASD algorithm and evaluated it empirically. Implementations can be found in https://github.com/lchenat/AX_exp. We manually tune the values of some parameters such as nmin and λ to boost the empirical performance, and then conducted experiments on a 4x4 Grid World environment. |
| Researcher Affiliation | Collaboration | 1University of Southern California 2Meta. Correspondence to: Liyu Chen <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: Layer-Aware State Discovery (LASD) |
| Open Source Code | Yes | Implementations can be found in https://github.com/lchenat/AX_exp. |
| Open Datasets | No | We manually tune the values of some parameters such as nmin and λ to boost the empirical performance, and then conducted experiments on a 4x4 Grid World environment. The learner has 5 actions in this environment: moving towards one of the four directions by a grid or reset to s0 (the upper left corner). When the learner takes a directional action, it has probability 0.9 of moving towards the corresponding direction, and 0.1 probability of randomly moving towards one of the four directions. |
| Dataset Splits | No | The paper does not specify any training/validation/test dataset splits, as it conducts experiments in a simulated 4x4 Grid World environment, which is not typically split like a dataset. |
| Hardware Specification | No | The paper mentions running empirical evaluations but does not provide specific details about the hardware used (e.g., CPU/GPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper provides a link to its implementation but does not specify any software dependencies with version numbers (e.g., programming language versions, library versions). |
| Experiment Setup | Yes | We manually tune the values of some parameters such as nmin and λ to boost the empirical performance, and then conducted experiments on a 4x4 Grid World environment. ... We run LASD on Grid World with L = 4, ϵ = 0.01, and δ = 0.001. |