no code implementations • 3 May 2024 • Riley Simmons-Edler, Ryan Badman, Shayne Longpre, Kanaka Rajan
The recent embrace of machine learning (ML) in the development of autonomous weapons systems (AWS) creates serious risks to geopolitical stability and the free exchange of ideas in AI research.
no code implementations • 10 Aug 2021 • Xiaoran Fan, Riley Simmons-Edler, Daewon Lee, Larry Jackel, Richard Howard, Daniel Lee
In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW.
no code implementations • 8 Jun 2021 • Vyacheslav Alipov, Riley Simmons-Edler, Nikita Putintsev, Pavel Kalinin, Dmitry Vetrov
Credit assignment is a fundamental problem in reinforcement learning, the problem of measuring an action's influence on future rewards.
no code implementations • 25 Sep 2019 • Riley Simmons-Edler, Ben Eisner, Daniel Yang, Anthony Bisulco, Eric Mitchell, Sebastian Seung, Daniel Lee
We implement the objective with an adversarial Q-learning method in which Q and Qx are the action-value functions for extrinsic and secondary rewards, respectively.
no code implementations • 19 Jun 2019 • Riley Simmons-Edler, Ben Eisner, Daniel Yang, Anthony Bisulco, Eric Mitchell, Sebastian Seung, Daniel Lee
We then propose a deep reinforcement learning method, QXplore, which exploits the temporal difference error of a Q-function to solve hard exploration tasks in high-dimensional MDPs.
no code implementations • 25 Mar 2019 • Riley Simmons-Edler, Ben Eisner, Eric Mitchell, Sebastian Seung, Daniel Lee
CGP aims to combine the stability and performance of iterative sampling policies with the low computational cost of a policy network.
no code implementations • 8 Jun 2018 • Riley Simmons-Edler, Anders Miltner, Sebastian Seung
Program Synthesis is the task of generating a program from a provided specification.