5 citations · 7 across the 2 of their papers we have counts for
4 papers
Continual Auxiliary Task Learning
Matthew McLeod, Chunlok Lo, Matthew Schlegel +4
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems. A variety of off-policy learning algorithms hav…
The Utility of Sparse Representations for Control in Reinforcement Learning
Vincent Liu, Raksha Kumaraswamy, Lei Le +1
We investigate sparse representations for control in reinforcement learning. While these representations are widely used in computer vision, their prevalence in reinforcement learn…
Context-Dependent Upper-Confidence Bounds for Directed Exploration
Raksha Kumaraswamy, Matthew Schlegel, Adam White +1
Directed exploration strategies for reinforcement learning are critical for learning an optimal policy in a minimal number of interactions with the environment. Many algorithms use…
Learning Sparse Representations in Reinforcement Learning with Sparse Coding
Lei Le, Raksha Kumaraswamy, Martha White
A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of spar…