Layered State Discovery for Incremental Autonomous Exploration
Authors: Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 <liyuc@usc.edu>. |
| 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. |