When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
Authors: Lenart Treven, Bhavya , Yarden As, Florian Dorfler, Andreas Krause
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that state-of-the-art RL algorithms trained on TACOS drastically reduce the interaction amount over their discrete-time counterpart while retaining the same or improved performance, and exhibiting robustness over discretization frequency. |
| Researcher Affiliation | Academia | Lenart Treven , Bhavya Sukhija, Yarden As, Florian Dörfler, Andreas Krause ETH Zurich, Switzerland |
| Pseudocode | No | The paper provides mathematical formulations and definitions but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | We provide our implementation at https://github.com/lasgroup/TaCoS. |
| Open Datasets | Yes | We consider the RC car (Kabzan et al., 2020), Greenhouse (Tap, 2000), Pendulum, Reacher, Halfcheetah and Humanoid environments from Brax (Freeman et al., 2021). |
| Dataset Splits | No | The paper does not explicitly state the training, validation, or test dataset splits with specific percentages, counts, or methodology for reproduction. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running experiments (e.g., GPU/CPU models, memory, or cloud provider specifications). |
| Software Dependencies | No | The paper mentions the use of algorithms like SAC and PPO but does not provide specific version numbers for any software dependencies, libraries, or frameworks used. |
| Experiment Setup | Yes | We investigate both the bounded number of interactions and interaction cost settings in our experiments. In particular, we study how the bound K affects the performance of TACOS and compare it to the standard equidistant baseline. We further study the interplay between the stochasticity of the environments (magnitude of g ) and interaction costs and the influence of tminon TACOS. For all experiments in this section, we combine SAC with TACOS (SAC-TACOS). |