TextWorld-Coin-Collector

PyTorch implementation of papar Counting to Explore and Generalize in Text-based Games

Coin Collector

• Coin collector is a set of games, each game is a randomly connected chain of rooms, the agent's goal is to navigate through the path and pick up the coin.
• Modes: easy / medium / hard, amount of off-chain rooms.
• Levels: Length of optimal trajectory.
• Action space: {go, take} × {north, south, east, west, coin}​
• Environment ID: twcc_[mode]_level[level]_gamesize[#game]_step[max step]_seed[random seed]_[split], please check here for more details.

Requirements

• Python 3
• PyTorch 0.4
• TextWorld: install the coin_collector branch
  • pip install https://github.com/microsoft/TextWorld/archive/refs/heads/coin_collector.zip
• Install gym_textworld by pip install gym_textworld/.
• tensorboardX
  • pip install tensorboardX
• nltk + the punkt package:
  • pip install nltk pytest
  • python -c "import nltk; nltk.download('punkt')"

Game Generation

• Run tw-make.py <env_id> to generate games corresponding to games defined in config files.
  • E.g., tw-make.py twcc_easy_level10_gamesize100_step50_seed9_train.
• You can use scripts/check_for_duplicates.py to check duplicates between training and /test sets.

To Run

• LSTM-DQN: run python lstm_dqn_baseline/train_single_generate_agent.py -c lstm_dqn_baseline/config/.
• LSTM-DRQN: run python lstm_drqn_baseline/train_single_generate_agent.py -c lstm_drqn_baseline/config/.
• Configurations can be modified in the above two config files.

LICENSE

MIT

References

• TextWorld: A Learning Environment for Text-based Games
• Counting to Explore and Generalize in Text-based Games