424 citations · 504 across the 13 of their papers we have counts for
6 papers · 1 filter
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)…
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…