Can I read Towards Integrating Real-Time Crowd Advice with Reinforcement Learning on EtoBox?

Towards Integrating Real-Time Crowd Advice with Reinforcement Learning by Gabriel V. de la Cruz; Bei Peng; Walter S. Lasecki; Matthew E. Taylor is a scholarly article available to read on EtoBox.

What is Towards Integrating Real-Time Crowd Advice with Reinforcement Learning about?

Reinforcement learning is a powerful machine learning paradigm that allows agents to autonomously learn to maximize a scalar reward. However, it often suffers from poor initial performance and long learning times. This paper discusses how collecting on-line human feedback, both in real time and post hoc, can potentially improve the performance of such learning systems. We use the game Pac-Man to simulate a navigation setting and show that workers are able to accurately identify both when a sub-optimal action is executed, and what action should have been performed instead. Demonstrating that the crowd is capable of generating this input, and discussing the types of errors that occur, serves as a critical first step in designing systems that use this real-time feedback to improve systems' learning performance on-the-fly.

Author

Gabriel V. de la Cruz; Bei Peng; Walter S. Lasecki; Matthew E. Taylor

Publisher

ACM

Published

2015

Language

EN