Code and data for our EACL 2023 paper Self-imitation Learning for Action Generation in Text-based Games.

1. Set Up the Environment

   pip install torch==1.5.1 transformers==2.5.1 jericho fasttext wandb importlib_metadata
   python -m spacy download en_core_web_sm

2. Download Pretrained Models and Walkthrough Data

   Please download the pretrained GPT-2 model, which was trained by the princeton-nlp/calm-textgame, from the following link: Download GPT-2 Model.

   Once downloaded, place the model files in the following directory structure:

   ├── download-models
   │   ├── gpt2
   │   │   ├── config.json
   │   │   ├── merges.txt
   │   │   ├── pytorch_model.bin
   │   │   └── vocab.json
   │   └── jericho_walkthrough_data
   

   Please note that the jericho_walkthrough_data files is used for validating the accuracy of the language model after fine-tuning and does not participate in the training process.

3. Run the Training Script

   Initiate training with the following command:

   sh drrn/run_csm.sh

@inproceedings{shi-etal-2023-self,
    title = "Self-imitation Learning for Action Generation in Text-based Games",
    author = "Shi, Zijing  and
      Xu, Yunqiu  and
      Fang, Meng  and
      Chen, Ling",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-main.50",
    pages = "703--726",
}

We thank princeton-nlp/calm-textgame for providing the excellent ClubFloyd dataset and CALM codebase.