LeDeepChef Deep Reinforcement Learning Agent for Families of Text-Based Games
Authors: Leonard Adolphs, Thomas Hofmann7342-7349
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The agent participated in Microsoft Research's First Text World Problems: A Language and Reinforcement Learning Challenge and outperformed all but one competitor on the final test set. |
| Researcher Affiliation | Academia | Leonard Adolphs, Thomas Hofmann Department of Computer Science ETH Zurich {firstname.lastname}@inf.ethz.ch |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly labeled or presented in a structured format in the paper. |
| Open Source Code | Yes | The code to train the agent, as well as an exemplary walkthrough of the game (with the agent ranking next moves), can be found on Git Hub1. 1https://github.com/leox1v/First Text World Problems |
| Open Datasets | Yes | The agent participated in Microsoft Research's First Text World Problems: A Language and Reinforcement Learning Challenge and outperformed all but one competitor on the final test set. ... we compare it against several baselines on the competition’s training, validation, and test set. |
| Dataset Splits | Yes | Table 2:Results on the unseen set of validation and test games from the Text World Challenge. |
| Hardware Specification | No | No specific hardware (e.g., GPU/CPU models, memory) used for experiments is mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., PyTorch 1.x, Python 3.x) are mentioned in the paper. |
| Experiment Setup | No | The paper describes the model architecture and training objective but does not provide specific hyperparameter values like learning rate, batch size, or optimizer settings used for the experiments. |