no code implementations • 27 Jan 2024 • Kuleen Sasse, Samuel Barham, Efsun Sarioglu Kayi, Edward W. Staley
While large language models (LLMs) are extremely capable at text generation, their outputs are still distinguishable from human-authored text.
1 code implementation • 10 Nov 2023 • Jared Markowitz, Edward W. Staley
To facilitate efficient learning, policy gradient approaches to deep reinforcement learning (RL) are typically paired with variance reduction measures and strategies for making large but safe policy changes based on a batch of experiences.
no code implementations • 2 May 2022 • Edward W. Staley, Jared Markowitz
After training, the layer can be arbitrarily reduced in width to exchange performance for narrowness.
no code implementations • 1 Dec 2021 • Edward W. Staley, Chace Ashcraft, Benjamin Stoler, Jared Markowitz, Gautam Vallabha, Christopher Ratto, Kapil D. Katyal
Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time.
1 code implementation • 9 Mar 2021 • Edward W. Staley, Corban G. Rivera, Ashley J. Llorens
Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains.
no code implementations • 22 Jun 2020 • Corban G. Rivera, Katie M. Popek, Chace Ashcraft, Edward W. Staley, Kapil D. Katyal, Bart L. Paulhamus
In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO.
1 code implementation • 25 Feb 2020 • Corban G. Rivera, Olivia Lyons, Arielle Summitt, Ayman Fatima, Ji Pak, William Shao, Robert Chalmers, Aryeh Englander, Edward W. Staley, I-Jeng Wang, Ashley J. Llorens
In this work, we introduce the AI safety TanksWorld as an environment for AI safety research with three essential aspects: competing performance objectives, human-machine teaming, and multi-agent competition.