Sequential Transfer in Reinforcement Learning with a Generative Model
Authors: Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically verify our theoretical findings in simple simulated domains. |
| Researcher Affiliation | Academia | 1Politecnico di Milano, Milan, Italy. Correspondence to: Andrea Tirinzoni <andrea.tirinzoni@polimi.it>. |
| 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. |