7 citations · 12 across the 4 of their papers we have counts for
3 papers
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)…
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…