1 citations · 1 across the 3 of their papers we have counts for
4 papers
AltNet: Addressing the Plasticity-Stability Dilemma in Reinforcement Learning
Mansi Maheshwari, John C. Raisbeck, Bruno Castro da Silva
Artificial neural networks have shown remarkable success in supervised learning when trained on a single task using a fixed dataset. However, when neural networks are trained on a…
A Scalable Finite Difference Method for Deep Reinforcement Learning
Matthew Allen, John Raisbeck, Hakho Lee
Several low-bandwidth distributable black-box optimization algorithms in the family of finite differences such as Evolution Strategies have recently been shown to perform nearly as…
Agent Spaces
John C. Raisbeck, Matthew W. Allen, Hakho Lee
Exploration is one of the most important tasks in Reinforcement Learning, but it is not well-defined beyond finite problems in the Dynamic Programming paradigm (see Subsection 2.4)…
Evolution Strategies Converges to Finite Differences
John C. Raisbeck, Matthew Allen, Ralph Weissleder +2
Since the debut of Evolution Strategies (ES) as a tool for Reinforcement Learning by Salimans et al. 2017, there has been interest in determining the exact relationship between the…