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..
Learning Sparse Representations in Reinforcement Learning with Sparse Coding
Authors: Lei Le, Raksha Kumaraswamy, Martha White
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that it is key to use a supervised objective, rather than the more straightforward unsupervised sparse coding approach. We compare the learned representations to a canonical fixed sparse representation, called tile-coding, demonstrating that the sparse coding representation outperforms a wide variety of tilecoding representations. ... We conducted experiments in three benchmark RL domains Mountain Car, Puddle World and Acrobot. |
| Researcher Affiliation | Academia | Dept. of Computer Science Indiana University Bloomington, IN, USA EMAIL |
| Pseudocode | No | The paper describes the algorithm steps in paragraph text and equations but does not include a formally labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We conducted experiments in three benchmark RL domains Mountain Car, Puddle World and Acrobot [Sutton, 1996]. |
| Dataset Splits | Yes | For learning the SCo PE representations, regularization parameters were chosen using 5-fold cross-validation on 5000 training samples, with βφ = 0.1 fixed to give a reasonable level of sparsity. |
| Hardware Specification | No | The paper does not specify any details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions general software components like "Python" but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The regularization weights βB are chosen from {1 5, . . . , 1 1, 0}, based on lowest cumulative error. For convenience, βw is fixed to be the same as βB. For learning the SCo PE representations, regularization parameters were chosen using 5-fold cross-validation on 5000 training samples, with βφ = 0.1 fixed to give a reasonable level of sparsity. ... The dimension k = 100 is set to be smaller than for tile coding |