45 citations · 145 across the 15 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
Model-Invariant State Abstractions for Model-Based Reinforcement Learning
Manan Tomar, Amy Zhang, Roberto Calandra +2
Accuracy and generalization of dynamics models is key to the success of model-based reinforcement learning (MBRL). As the complexity of tasks increases, so does the sample ineffici…
Useful Policy Invariant Shaping from Arbitrary Advice
Paniz Behboudian, Yash Satsangi, Matthew E. Taylor +2
Reinforcement learning is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in com…
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy
Yunshu Du, Garrett Warnell, Assefaw Gebremedhin +2
Experience replay (ER) improves the data efficiency of off-policy reinforcement learning (RL) algorithms by allowing an agent to store and reuse its past experiences in a replay bu…