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Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning by Comanici, Gheorghe; Glaese, Amelia; Gergely, Anita; Toyama, Daniel; Ahmed, Zafarali; Jackson, Tyler; Hamel, Philippe; Precup, Doina is a scholarly article available to read on EtoBox.

What is Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning about?

Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks can invoke the solutions of lower-level tasks as if they were primitive actions. In this work, we study the utility of hierarchical decompositions for learning an appropriate way to interact with a complex interface. Specifically, we train HRL agents that can interface with applications in a simulated Android device. We introduce a Hierarchical Distributed Deep Reinforcement Learning architecture that learns (1) subtasks corresponding to simple finger gestures, and (2) how to combine these gestures to solve several Android tasks. Our approach relies on goal conditioning and can be used more generally to convert any base RL agent into an HRL agent. We use the AndroidEnv environment to evaluate our approach. For the experiments, the HRL agent uses a distributed version of the popular DQN algorithm to train different components of the hierarchy. While the native action space is completely intractable for simple DQN agents, our architecture can be used to establish an effective way to interact with different tasks, significantly improving t

Author

Comanici, Gheorghe; Glaese, Amelia; Gergely, Anita; Toyama, Daniel; Ahmed, Zafarali; Jackson, Tyler; Hamel, Philippe; Precup, Doina

Published

2022

Language

EN