Causally Aligned Curriculum Learning
Authors: Mingxuan Li, Junzhe Zhang, Elias Bareinboim
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate our proposed methodology through experiments in discrete and continuous confounded tasks with pixel observations. and Finally, we validate the proposed framework through extensive experiments in various decision-making tasks. |
| Researcher Affiliation | Academia | Causal Artificial Intelligence Lab, Columbia University, USA |
| Pseudocode | Yes | Algorithm 1: FINDMAXEDIT, Algorithm 2: CURRICULUM LEARNING, Algorithm 3: FINDCAUSALCURRICULUM, Algorithm 4: ISEDIT, Algorithm 5: LISTEDIT, Algorithm 6: CAUSAL CURRICULUM LEARNING |
| Open Source Code | No | No explicit statement for open-source code release for their own work, nor a direct link to their repository, was found. The paper mentions using "Klink et al. (2022) s official implementations (https://github.com/psclklnk/currot)" for curriculum generators, which is third-party code. |
| Open Datasets | Yes | Colored Sokoban and Button Maze are implemented based on Sokoban (Schrader, 2018) and Grid World (Chevalier-Boisvert et al., 2018), respectively. |
| Dataset Splits | No | No explicit details about train/validation/test dataset splits (percentages, counts, or predefined splits) were provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Py Torch' for implementation but does not specify a version number. Other software dependencies are not listed with version numbers. |
| Experiment Setup | Yes | Both networks have three convolutional layers and two linear layers with rectified linear unit (Re LU) activation functions. The number of output channels of the convolutional layers is [32, 64, 64] with each layer. For convolutional kernels in those three convolutional layers, we use 8 × 8 with a stride of 4, 4 × 4 with a stride of 2, and 3 × 3 with a stride of 1, respectively. We flatten the output of the convolutional layers and feed it into the two linear layers with the intermediate feature dimension set to 512. The input for the network is an image observation of size 84 × 84 × 3 for both environments. |