HEX-RL

This code accompanies the paper Inherently Explainable Reinforcement Learning in Natural Language.

🤔 Explainable RL

cd qbert/extraction && gunicorn --workers 4 --bind 0.0.0.0:5000 wsgi:app
redis-server

• Open another terminal:

cd qbert && python train.py --training_type base --reward_type game_only  --subKG_type QBert

nohup python train.py --training_type chained --reward_type game_and_IM  --subKG_type QBert --batch_size 2 --seed 0 --preload_weights Q-BERT/qbert/logs/qbert.pt --eval_mode --graph_dropout 0 --mask_dropout 0 --dropout_ratio 0

👁️‍🗨️ Features

• --subKG_type: What kind of subgraph you want to use. There are 3 choices, 'Full', 'SHA', 'QBert'.

  • 'Full': 4 subgraphs are all full graph_state.
  • 'QBert':
    1. __ 'is' __ (Attr of objects)
    2. 'you' 'have' __
    3. __ 'in' __
    4. others (direction)
  • 'SHA':
    1. room connectivity (history included)
    2. what's in current room
    3. your inventory
    4. remove you related nodes (history included)

• --eval_mode: Whether turning off the training and evaluation the pre-trained model

  • bool. True or False
  • use --preload_weights at the same time.

• --random_action: Whether to use random valid actions instead of QBERT actions.

  • bool. True or False

Debug Tricks

1. graph_dropout to .5 and mask_dropout to .5 in train.py.
2. The score should reach 5 in 10,000 steps.