680 citations · 1.3k across the 33 of their papers we have counts for
37 papers
Can You Improve My Code? Optimizing Programs with Local Search
Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor +1
This paper introduces a local search method for improving an existing program with respect to a measurable objective. Program Optimization with Locally Improving Search (POLIS) exp…
Augmenting Flight Training with AI to Efficiently Train Pilots
Michael Guevarra, Srijita Das, Christabel Wayllace +3
We propose an AI-based pilot trainer to help students learn how to fly aircraft. First, an AI agent uses behavioral cloning to learn flying maneuvers from qualified flight instruct…
Decentralized Mean Field Games
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley +1
Multiagent reinforcement learning algorithms have not been widely adopted in large scale environments with many agents as they often scale poorly with the number of agents. Using m…
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning
Volodymyr Tkachuk, Sriram Ganapathi Subramanian, Matthew E. Taylor
Some reinforcement learning methods suffer from high sample complexity causing them to not be practical in real-world situations. -function reuse, a transfer learning method, is…
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Yaodong Yang, Jun Luo, Ying Wen +5
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum f…
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym
Matthew E. Taylor, Nicholas Nissen, Yuan Wang +1
Reinforcement learning (RL) is a popular machine learning paradigm for game playing, robotics control, and other sequential decision tasks. However, RL agents often have long learn…