Contextual Action Language Model (CALM) and the ClubFloyd Dataset

Code and data for paper Keep CALM and Explore: Language Models for Action Generation in Text-based Games at EMNLP 2020.

Overview

Our ClubFloyd dataset (calm/lm_data.zip) is crawled from the ClubFloyd website and contains 426 human gameplay transcripts, which cover 590 text-based games of diverse genres and styles.

The data consists of 223,527 context-action pairs in the format [CLS] observation [SEP] action [SEP] next observation [SEP] next action [SEP]. We use [CLS] observation [SEP] action [SEP] next observation [SEP] as the context to train language models (n-gram, GPT-2) to predict next action [SEP], and show that this action generation ability generalizes to unseen games and supports gameplay when combined with reinforcement learning.

Getting Started

• Clone repo and install dependencies:

pip install torch==1.4 transformers==2.5.1 jericho fasttext wandb importlib_metadata
git clone https://github.com/princeton-nlp/calm-textgame && cd calm-textgame
ln -s ../lm calm && ln -s ../lm drrn

(If the pip installation fails for fasttext, try the build steps here: https://github.com/facebookresearch/fastText#building-fasttext-for-python)

• Train CALM:

  cd calm
  unzip lm_data.zip
  python train.py

Trained model weights can be downloaded here for both GPT-2 and n-gram models.

• Then train DRRN using the trained CALM:

  cd ../drrn
  python train.py --rom_path ../games/${GAME} --lm_path ${PATH_TO_CALM} --lm_type ${gpt_or_ngram}

• To quickly try out the GPT-2 CALM model:

  from lm import GPT2LM
  model = GPT2LM("model_weights/gpt2")
  print(model.generate("[CLS] observation [SEP] action [SEP] next observation [SEP]", k=30))

  Citation

  @inproceedings{yao2020calm,
  title={Keep CALM and Explore: Language Models for Action Generation in Text-based Games},
  author={Yao, Shunyu and Rao, Rohan and Hausknecht, Matthew and Narasimhan, Karthik},
  booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
  year={2020}
  }

  Acknowledgements

  Thanks Jacqueline for hosting the wonderful ClubFloyd website and granting our use!

The code borrows from TDQN (for the RL part) and Huggingface Transformers (for the CALM part).

For any questions please contact Shunyu Yao <shunyuyao.cs@gmail.com>.