536 citations · 2k across the 52 of their papers we have counts for
7 papers · 1 filter
Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds
Zhiyu Lin, Brent Harrison, Aaron Keech +1
We describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement lea…
Guiding Reinforcement Learning Exploration Using Natural Language
Brent Harrison, Upol Ehsan, Mark O. Riedl
In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specif…
A Framework for Exploring and Evaluating Mechanics in Human Computation Games
Kristin Siu, Alexander Zook, Mark O. Riedl
Human computation games (HCGs) are a crowdsourcing approach to solving computationally-intractable tasks using games. In this paper, we describe the need for generalizable HCG desi…
Event Representations for Automated Story Generation with Deep Neural Nets
Lara J. Martin, Prithviraj Ammanabrolu, Xinyu Wang +4
Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can gener…
Evaluating Singleplayer and Multiplayer in Human Computation Games
Kristin Siu, Matthew Guzdial, Mark O. Riedl
Human computation games (HCGs) can provide novel solutions to intractable computational problems, help enable scientific breakthroughs, and provide datasets for artificial intellig…
Enter the Matrix: Safely Interruptible Autonomous Systems via Virtualization
Mark O. Riedl, Brent Harrison
Autonomous systems that operate around humans will likely always rely on kill switches that stop their execution and allow them to be remote-controlled for the safety of humans or…