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..
Zero-Shot Reinforcement Learning via Function Encoders
Authors: Tyler Ingebrand, Amy Zhang, Ufuk Topcu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate state-of-the-art data efficiency, asymptotic performance, and training stability in three RL fields by augmenting basic RL algorithms with a function encoder task representation. |
| Researcher Affiliation | Academia | 1University of Texas at Austin. Correspondence to: Tyler Ingebrand <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Function Encoder |
| Open Source Code | Yes | Code: https://github.com/tyler-ingebrand/Function Encoder RL |
| Open Datasets | Yes | The testing environment is a modified Half-Cheetah environment (Towers et al., 2023) where the segment lengths, friction coefficient, and control authority are randomized within a range each episode... |
| Dataset Splits | No | The paper mentions 'training dataset' and 'testing environment' but does not provide explicit information about validation splits (e.g., percentages or sample counts for training, validation, and test sets). |
| Hardware Specification | Yes | Hardware All experiments on performed on a 9th generation Intel i9 CPU and a Nvidia Geforce 2060 with 6 GB of memory. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). It mentions 'transformers' as a class of models but not specific library versions. |
| Experiment Setup | Yes | We use b = 100 basis functions for all experiments. See A.6 for an ablation on how the hyper-parameters affect performance. |