Zero-Shot Reinforcement Learning via Function Encoders
Authors: Tyler Ingebrand, Amy Zhang, Ufuk Topcu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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 <tyleringebrand@utexas.edu>. |
| 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. |