activity
20152026
most citedLearning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning

45 citations · 116 across the 26 of their papers we have counts for

collaborators
Showing 2022Show all

10 papers · 1 filter

cs.HC2022★ 2 cited

Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses

Michael R. Dawson, Adam S. R. Parker, Heather E. Williams +4

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple…

cs.AI2022

Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have

Nadia M. Ady, Roshan Shariff, Johannes Günther +1

Curiosity for machine agents has been a focus of lively research activity. The study of human and animal curiosity, particularly specific curiosity, has unearthed several propertie…

cs.MA2022★ 2 cited

Over-communicate no more: Situated RL agents learn concise communication protocols

Aleksandra Kalinowska, Elnaz Davoodi, Florian Strub +5

While it is known that communication facilitates cooperation in multi-agent settings, it is unclear how to design artificial agents that can learn to effectively and efficiently co…

cs.AI2022★ 5 cited

Adaptive patch foraging in deep reinforcement learning agents

Nathan J. Wispinski, Andrew Butcher, Kory W. Mathewson +3

Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biological intelligence, this behavioral optimiz…

cs.AI2022★ 7 cited

The Alberta Plan for AI Research

Richard S. Sutton, Michael Bowling, Patrick M. Pilarski

Herein we describe our approach to artificial intelligence research, which we call the Alberta Plan. The Alberta Plan is pursued within our research groups in Alberta and by others…

cs.LG2022

What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience

Alexandra Kearney, Anna Koop, Johannes Günther +1

In computational reinforcement learning, a growing body of work seeks to construct an agent's perception of the world through predictions of future sensations; predictions about en…