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..
Online Reinforcement Learning with Uncertain Episode Lengths
Authors: Debmalya Mandal, Goran Radanovic, Jiarui Gan, Adish Singla, Rupak Majumdar
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we compare our learning algorithms with existing value-iteration based episodic RL algorithms on a grid-world environment. Experiments We evaluated the performance of our algorithm on the Taxi environment, a 5 5 grid-world environment introduced by (Dietterich 2000). |
| Researcher Affiliation | Academia | 1Max Planck Institute for Software Systems 2 University of Oxford EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | ALGORITHM 1: UCB-VI Generalized and ALGORITHM 2: Estimating Unknown Discount Factor |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluated the performance of our algorithm on the Taxi environment, a 5 5 grid-world environment introduced by (Dietterich 2000). |
| Dataset Splits | No | The paper mentions evaluating performance on the Taxi environment for 100 episodes but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We considered 100 episodes and each episode length was generated uniformly at random from the following distributions. For the geometric discounting, we show γ = 0.9, 0.95 and 0.975. |