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..
Sequential Transfer in Reinforcement Learning with a Generative Model
Authors: Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically verify our theoretical ๏ฌndings in simple simulated domains. |
| Researcher Affiliation | Academia | 1Politecnico di Milano, Milan, Italy. Correspondence to: Andrea Tirinzoni <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Policy Transfer from Uncertain Models (PTUM) |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | the 4-rooms domain of Sutton et al. (1999) with only two rooms) |
| Dataset Splits | No | The paper describes experimental setups with different tasks and domains (e.g., grid-world, object-world) but does not refer to traditional dataset splits like 'training', 'validation', or 'test' sets in the context of supervised learning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We adopt a standard 12 12 grid-world divided into two parts by a vertical wall (i.e., the 4-rooms domain of Sutton et al. (1999) with only two rooms). The agent starts in the left room and must reach a goal state in the right one, with the two rooms connected by a door. We consider 12 different tasks, whose models are known to the agent, with different goal and door locations. |