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]

Demo

Trained multitask agent: https://www.youtube.com/watch?v=eGzTDIvX4IY
Facebook login: https://www.youtube.com/watch?v=IQytRUKmWhs&t=2s

Requirement

Need to download selenium & install chrome driver for selenium..

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
  file://"your-path-to-miniwob-plusplus"/html/miniwob
  E.g. "your-path-to-miniwob-plusplus" is "/h/sheng/DOM-Q-NET/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.

Multitask Assumptions

State & Action restrictions

Attribute embeddings & vocabulary

• UNKnown tokens
  These are assigned to a random vector such that the cosine distance with the text attribute can yield 1.0 for the direct alignment.

Acknowledgement

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