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..
$\mathrm{SO}(2)$-Equivariant Reinforcement Learning
Authors: Dian Wang, Robin Walters, Robert Platt
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments that demonstrate that our equivariant versions of DQN and SAC can be significantly more sample efficient than competing algorithms on an important class of robotic manipulation problems. |
| Researcher Affiliation | Academia | Dian Wang, Robin Walters, and Robert Platt Khoury College of Computer Sciences Northeastern University Boston, MA 02115, USA EMAIL |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Supplementary video and code are available at https://pointw.github.io/equi_rl_page/. |
| Open Datasets | No | The paper describes experimental environments implemented in the Py Bullet simulator and mentions pre-populating replay buffers with expert demonstrations, but it does not provide a public link, DOI, or formal citation for a static, downloadable dataset used for training or for the expert demonstrations. |
| Dataset Splits | No | The paper details training and evaluation procedures but does not explicitly mention distinct validation dataset splits or methodology (e.g., specific percentages or counts for a validation set) beyond general training/testing cycles. |
| Hardware Specification | No | The paper mentions using Py Bullet simulator and running 5 parallel environments but does not specify any particular hardware components such as GPU or CPU models, or memory. |
| Software Dependencies | No | The paper mentions using 'E2CNN library with Py Torch' and 'Py Bullet simulator' but does not provide specific version numbers for these or other software dependencies necessary for replication. |
| Experiment Setup | Yes | In the DQN experiments, we use the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 10^-4. We use Huber loss (Huber, 1964) for calculating the TD loss. We use a discount factor γ = 0.95. The batch size is 32. The buffer has a capacity of 100,000 transitions. In the SAC (and SACfD) experiments, we use the Adam optimizer with a learning rate of 10^-3. The entropy temperature α is initialized at 10^-2. The target entropy is -5. The discount factor γ = 0.99. The batch size is 64. The buffer has a capacity of 100,000 transitions. |