[DOM-Q-NET] Grounded RL on Structured Language | AISC Author Speaking

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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For more details including paper and slides, visit https://aisc.a-i.science/events/2019-04-01/

Lead/first author: Sheng Jia
Facilitator: Nicolai Pogrebnyakov

Abstract

Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning. However, web navigation tasks are difficult for current deep reinforcement learning (RL) models due to the large discrete action space and the varying number of actions between the states. In this work, we introduce DOM-Q-NET, a novel architecture for RL-based web navigation to address both of these problems. It parametrizes Q functions with separate networks for different action categories: clicking a DOM element and typing a string input. Our model utilizes a graph neural network to represent the tree-structured HTML of a standard web page. We demonstrate the capabilities of our model on the MiniWoB environment where we can match or outperform existing work without the use of expert demonstrations. Furthermore, we show 2x improvements in sample efficiency when training in the multi-task setting, allowing our model to transfer learned behaviours across tasks.



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Tags:
deep learning
machine learning
gcn
deep reinforcement learning
learning representation
dom-net
dom-q-net
graph convolutional neural network