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..
Continuous-time Model-based Reinforcement Learning
Authors: Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement and test our method on a new ODE-RL suite that explicitly solves continuous-time control systems. Our experiments illustrate that the model is robust against irregular and noisy data, is sampleefficient, and can solve control problems which pose challenges to discrete-time MBRL methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Aalto University, Finland. |
| Pseudocode | Yes | Algorithm 1 Continuous-Time MBRL with Neural ODEs |
| Open Source Code | Yes | Our model implementation can be found at https://github.com/cagatayyildiz/oderl |
| Open Datasets | Yes | The test environments are already implemented in standard RL frameworks such as Open AI Gym (Brockman et al., 2016) and Deep Mind Control Suite (Tassa et al., 2018). |
| Dataset Splits | No | The paper mentions "train and test one-step ahead prediction errors" but does not specify explicit validation splits (e.g., percentages or counts) or refer to a standard validation methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models, memory, or specific cloud instance types. |
| Software Dependencies | No | The paper states "We implement our method in Py Torch (Paszke et al., 2019)" but does not specify the version number of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In the remainder of this paper, we fix H = 2.0 and nens = 10. |