DOM-Q-NET: Grounded RL on Structured Language

"DOM-Q-NET: Grounded RL on Structured Language" International Conference on Learning Representations (2019). Sheng Jia, Jamie Kiros, Jimmy Ba. [arxiv] [openreview]

Installation

1. Clone this repo
2. Download MiniWoB++ environment from the original repo https://github.com/stanfordnlp/miniwob-plusplus
   and copy miniwob-plusplus/html folder to miniwob/html in this repo
   
3. In fact, this html folder could be stored anywhere, but remember to perform one of the following actions:
   

• Set environment variable "WOB_PATH" to
  "your-path-to-miniwob-plusplus"/html/miniwob
  
• Directly modify the base_url on line 33 of instance.py to
  "your-path-to-miniwob-plusplus"/html/miniwob
  In my case, base_url='file:///h/sheng/DOM-Q-NET/miniwob/html/miniwob/'
  

Run experiment

Experiment launch files are stored under runs For example,

cd runs/hard2medium9tasks/
sh run1.sh

will launch a 11 multi-task (social-media search-engine login-user enter-password click-checkboxes click-option enter-dynamic-text enter-text email-inbox-delete click-tab-2 navigation-tree) experiment.

Acknowledgement

Credit to Dopamine for the implementation of prioritized replay used in dstructs/dopamine_segtree.py