Fast active learning for pure exploration in reinforcement learning
Authors: Pierre Menard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we illustrate how 1/n bonuses can be beneficial for exploration in Appendix G. |
| Researcher Affiliation | Collaboration | 1Otto von Guericke University 2Inria 3Université de Lille 4Universitat Pompeu Fabra 5Deep Mind Paris. |
| Pseudocode | Yes | Algorithm 1 RF-Express |
| Open Source Code | No | "rlberry A Reinforcement Learning Library for Research and Education. https://github.com/rlberry-py/rlberry, 2021a." This is a general library, not explicitly stated to be the source code for the methodology in this specific paper. |
| Open Datasets | No | No, the paper is theoretical and focuses on algorithm design and sample complexity bounds for reinforcement learning in MDPs. It does not describe experiments using a specific dataset that would require access information for training. |
| Dataset Splits | No | No, the paper is theoretical and does not describe experiments that involve dataset splits for validation. The focus is on theoretical bounds and algorithm design. |
| Hardware Specification | No | No, the paper is theoretical and does not describe hardware used for experiments. The provided text focuses on algorithmic design and theoretical analysis. |
| Software Dependencies | No | No, the paper is theoretical and focuses on mathematical proofs and algorithms. It does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | No, the paper is theoretical and does not describe specific experimental setups, hyperparameters, or training configurations. The content focuses on algorithm descriptions and theoretical properties. |