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 [1].
Introducing Symmetries to Black Box Meta Reinforcement Learning
Authors: Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh, Yutian Chen7202-7210
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate Sym LA on bandits, classic control, and grid worlds, comparing generalisation capabilities to Meta RNNs. Sym LA improves generalisation when varying action dimensions, permuting observations and actions, and significantly changing tasks and environments. |
| Researcher Affiliation | Collaboration | Louis Kirsch1 2, Sebastian Flennerhag1, Hado van Hasselt1, Abram Friesen1, Junhyuk Oh1, Yutian Chen1 1 DeepMind 2 The Swiss AI Lab IDSIA, USI, SUPSI |
| Pseudocode | Yes | For pseudo code, see Algorithm 1 in the appendix. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We first compare Sym LA and the Meta RNN on the two-armed (dependent) bandit experiments from Wang et al. (2016) |
| Dataset Splits | No | The paper discusses 'meta-training' and 'meta-testing' environments and 'meta-training distribution' but does not specify numerical train/validation/test splits (e.g., percentages or sample counts) for the datasets used. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) that would be required for replication. |
| Experiment Setup | Yes | Hyper-parameters are in Appendix B. |