31 citations · 72 across the 22 of their papers we have counts for
4 papers · 1 filter
The Value of Information When Deciding What to Learn
Dilip Arumugam, Benjamin Van Roy
All sequential decision-making agents explore so as to acquire knowledge about a particular target. It is often the responsibility of the agent designer to construct this target wh…
Bad-Policy Density: A Measure of Reinforcement Learning Hardness
David Abel, Cameron Allen, Dilip Arumugam +3
Reinforcement learning is hard in general. Yet, in many specific environments, learning is easy. What makes learning easy in one environment, but difficult in another? We address t…
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning
Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon
How do we formalize the challenge of credit assignment in reinforcement learning? Common intuition would draw attention to reward sparsity as a key contributor to difficult credit…
Deciding What to Learn: A Rate-Distortion Approach
Dilip Arumugam, Benjamin Van Roy
Agents that learn to select optimal actions represent a prominent focus of the sequential decision-making literature. In the face of a complex environment or constraints on time an…