Working Memory Graphs
Authors: Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew Hausknecht
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate WMG in three environments featuring factored observation spaces: a Pathfinding environment that requires complex reasoning over past observations, Baby AI gridworld levels that involve variable goals, and Sokoban which emphasizes future planning. We find that the combination of WMG s Transformer-based architecture with factored observation spaces leads to significant gains in learning efficiency compared to baseline architectures across all tasks. |
| Researcher Affiliation | Industry | 1Microsoft Research AI, Redmond, Washington, USA. |
| Pseudocode | No | The paper describes the WMG model and training process using text and mathematical equations, but it does not include an explicitly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | To encourage further work and comparative studies, we provide WMG s source code and pre-trained models at https://github.com/microsoft/wmg_agent. |
| Open Datasets | Yes | We evaluate WMG in three environments featuring factored observation spaces: a Pathfinding environment..., Baby AI gridworld levels..., and Sokoban... Baby AI domain (Chevalier-Boisvert et al., 2018)... Sokoban (Guez et al., 2019). |
| Dataset Splits | No | The paper mentions hyperparameter tuning and |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper describes the model and training process but does not specify any software dependencies with version numbers (e.g., Python version, PyTorch/TensorFlow version, specific library versions). |
| Experiment Setup | Yes | For this task, WMG is configured with Memos but no Factors. The number of Memos is a tuned hyperparameter, equal to 16 in this experiment. (See Appendix C for all settings.) |