123 citations · 358 across the 19 of their papers we have counts for
21 papers
Agency Is Frame-Dependent
David Abel, André Barreto, Michael Bowling +13
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determini…
Three Dogmas of Reinforcement Learning
David Abel, Mark K. Ho, Anna Harutyunyan
Modern reinforcement learning has been conditioned by at least three dogmas. The first is the environment spotlight, which refers to our tendency to focus on modeling environments…
On the Convergence of Bounded Agents
David Abel, André Barreto, Hado van Hasselt +3
When has an agent converged? Standard models of the reinforcement learning problem give rise to a straightforward definition of convergence: An agent converges when its behavior or…
A Definition of Continual Reinforcement Learning
David Abel, André Barreto, Benjamin Van Roy +3
In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward. However, this perspective is based on…
Settling the Reward Hypothesis
Michael Bowling, John D. Martin, David Abel +1
The reward hypothesis posits that, "all of what we mean by goals and purposes can be well thought of as maximization of the expected value of the cumulative sum of a received scala…
Meta-Gradients in Non-Stationary Environments
Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker +3
Meta-gradient methods (Xu et al., 2018; Zahavy et al., 2020) offer a promising solution to the problem of hyperparameter selection and adaptation in non-stationary reinforcement le…