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..
Learning Subgoal Representations with Slow Dynamics
Authors: Siyuan Li, Lulu Zheng, Jianhao Wang, Chongjie Zhang
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to compare our approach to existing state-of-the-art methods in HRL and in efficient exploration. |
| Researcher Affiliation | Academia | Institute for Interdisciplinary Information Sciences Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1 LESSON algorithm |
| Open Source Code | Yes | Find open-source code at https://github.com/Siyuan Lee/LESSON |
| Open Datasets | Yes | We compare LESSON with state-of-theart HRL and exploration methods on complex Mu Jo Co tasks (Todorov et al., 2012). |
| Dataset Splits | No | The paper evaluates performance during training but does not specify a separate validation dataset split or strategy for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions software like SAC, Adam optimizer, and MuJoCo, but does not provide specific version numbers for any of these components or other libraries. |
| Experiment Setup | Yes | Discount factor γ = 0.99 for both levels. Adam optimizer; learning rate 0.0002. Soft update targets τ = 0.005 for both levels. Replay buffer of size 1e6 for both levels. Reward scaling of 0.1 for both levels. Entropy coefficient of SAC α = 0.2 for both levels. Low-level policy length c = 10 for the Point robot and c = 20 for the Ant robot except for the Ant Push task. In the Ant Push task, c = 50. Subgoal dimension of size 2. |