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..
TAAC: Temporally Abstract Actor-Critic for Continuous Control
Authors: Haonan Yu, Wei Xu, Haichao Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate TAAC s advantages over several strong baselines across 14 continuous control tasks. |
| Researcher Affiliation | Industry | Haonan Yu, Wei Xu, Haichao Zhang Horizon Robotics Cupertino, CA 95014 EMAIL |
| Pseudocode | Yes | The overall TAAC algorithm is summarized in Algorithm 1 Appendix A. |
| Open Source Code | Yes | Code is available at https://github.com/hnyu/taac. |
| Open Datasets | Yes | a) Simple Control: Three control tasks (Brockman et al., 2016) with small action and observation spaces: Mountain Car Continuous, Lunar Lander Continuous, and Inverted Double Pendulum ; b) Locomotion: Four locomotion tasks (Brockman et al., 2016) that feature complex physics and action spaces: Hopper, Ant, Walker2d, and Half Cheetah; d) Manipulation: Four Fetch (Plappert et al., 2018) tasks with sparse rewards and hard exploration (reward given only upon success): Fetch Reach, Fetch Push, Fetch Slide, and Fetch Pick And Place; e) Driving: One CARLA autonomous-driving task (Dosovitskiy et al., 2017) that has complex high-dimensional multi-modal sensor inputs (camera, radar, IMU, collision, GPS, etc.): Town01. |
| Dataset Splits | No | The paper does not provide explicit details about training, validation, and test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | Crucially, for fair comparisons we also make each method train 1) for the same number of gradient steps, 2) with the same mini-batch size and learning rate, 3) using roughly the same number of weights, and 4) with a common set of hyperparameters (tuned with vanilla SAC) for the SAC backbone . More details of the experimental settings are described in Appendix J. In our experiments, we set the repeating hyperparameter N to 3 on Simple Control, Locomotion and Manipulation, and to 5 on Terrain and Driving. |