  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.
