424 citations · 611 across the 16 of their papers we have counts for
18 papers
The Open Ant: A Robot Platform for Reinforcement Learning Research
Elena Sorina Lupu, Patrick Spieler, Khurram Javed +4
Reinforcement learning (RL) research has demonstrated success in both physical and simulated domains; however, the predominant methodology remains rooted in simulations. The predom…
Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots
Khurram Javed, Joseph Modayil, Gloria Kennickell +2
We built a robot called the Robotroller that actuates an Atari CX40+ controller and a device called the Atari Devbox that renders the game frame and the reward signal from the Arca…
The Ungrounded Alignment Problem
Marc Pickett, Aakash Kumar Nain, Joseph Modayil +1
Modern machine learning systems have demonstrated substantial abilities with methods that either embrace or ignore human-provided knowledge, but combining benefits of both styles r…
Towards model-free RL algorithms that scale well with unstructured data
Joseph Modayil, Zaheer Abbas
Conventional reinforcement learning (RL) algorithms exhibit broad generality in their theoretical formulation and high performance on several challenging domains when combined with…
Loss of Plasticity in Continual Deep Reinforcement Learning
Zaheer Abbas, Rosie Zhao, Joseph Modayil +2
The ability to learn continually is essential in a complex and changing world. In this paper, we characterize the behavior of canonical value-based deep reinforcement learning (RL)…
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents
Patrick M. Pilarski, Andrew Butcher, Elnaz Davoodi +7
Learned communication between agents is a powerful tool when approaching decision-making problems that are hard to overcome by any single agent in isolation. However, continual coo…
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making
Andrew Butcher, Michael Bradley Johanson, Elnaz Davoodi +6
In this paper, we contribute a multi-faceted study into Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making…
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study
Dylan J. A. Brenneis, Adam S. Parker, Michael Bradley Johanson +7
Artificial intelligence systems increasingly involve continual learning to enable flexibility in general situations that are not encountered during system training. Human interacti…
Adapting the Function Approximation Architecture in Online Reinforcement Learning
John D. Martin, Joseph Modayil
The performance of a reinforcement learning (RL) system depends on the computational architecture used to approximate a value function. Deep learning methods provide both optimizat…
On Inductive Biases in Deep Reinforcement Learning
Matteo Hessel, Hado van Hasselt, Joseph Modayil +1
Many deep reinforcement learning algorithms contain inductive biases that sculpt the agent's objective and its interface to the environment. These inductive biases can take many fo…
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Tom Schaul, Diana Borsa, Joseph Modayil +1
Rather than proposing a new method, this paper investigates an issue present in existing learning algorithms. We study the learning dynamics of reinforcement learning (RL), specifi…
Deep Reinforcement Learning and the Deadly Triad
Hado van Hasselt, Yotam Doron, Florian Strub +3
We know from reinforcement learning theory that temporal difference learning can fail in certain cases. Sutton and Barto (2018) identify a deadly triad of function approximation, b…
The Barbados 2018 List of Open Issues in Continual Learning
Tom Schaul, Hado van Hasselt, Joseph Modayil +7
We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments. The purpose o…
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017
M. Botvinick, D. G. T. Barrett, P. Battaglia +16
We agree with Lake and colleagues on their list of key ingredients for building humanlike intelligence, including the idea that model-based reasoning is essential. However, we favo…
Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt +7
The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can…
Building Machines That Learn and Think Like People
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum +1
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks t…
Scaling Life-long Off-policy Learning
Adam White, Joseph Modayil, Richard S. Sutton
We pursue a life-long learning approach to artificial intelligence that makes extensive use of reinforcement learning algorithms. We build on our prior work with general value func…
Multi-timescale Nexting in a Reinforcement Learning Robot
Joseph Modayil, Adam White, Richard S. Sutton
The term "nexting" has been used by psychologists to refer to the propensity of people and many other animals to continually predict what will happen next in an immediate, local, a…