Continuous-time Model-based Reinforcement Learning
Authors: Cagatay Yildiz, Markus Heinonen, Harri Lähdesmäki
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |