5 citations · 14 across the 8 of their papers we have counts for
10 papers
Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control
Sean Anderson, Katie Byl, João P. Hespanha
Learning for control in repeated tasks allows for well-designed experiments to gather the most useful data. We consider the setting in which we use a data-driven controller that do…
Leveraging Reward Gradients For Reinforcement Learning in Differentiable Physics Simulations
Sean Gillen, Katie Byl
In recent years, fully differentiable rigid body physics simulators have been developed, which can be used to simulate a wide range of robotic systems. In the context of reinforcem…
Direct Random Search for Fine Tuning of Deep Reinforcement Learning Policies
Sean Gillen, Asutay Ozmen, Katie Byl
Researchers have demonstrated that Deep Reinforcement Learning (DRL) is a powerful tool for finding policies that perform well on complex robotic systems. However, these policies a…
Combining Deep Reinforcement Learning And Local Control For The Acrobot Swing-up And Balance Task
Sean Gillen, Marco Molnar, Katie Byl
In this work we present a novel extension of soft actor critic, a state of the art deep reinforcement algorithm. Our method allows us to combine traditional controllers with learne…
Mesh Based Analysis of Low Fractal Dimension Reinforcement Learning Policies
Sean Gillen, Katie Byl
In previous work, using a process we call meshing, the reachable state spaces for various continuous and hybrid systems were approximated as a discrete set of states which can then…
Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement Learning
Sean Gillen, Katie Byl
A key limitation in using various modern methods of machine learning in developing feedback control policies is the lack of appropriate methodologies to analyze their long-term dyn…
Combining Benefits from Trajectory Optimization and Deep Reinforcement Learning
Guillaume Bellegarda, Katie Byl
Recent breakthroughs both in reinforcement learning and trajectory optimization have made significant advances towards real world robotic system deployment. Reinforcement learning…
Training in Task Space to Speed Up and Guide Reinforcement Learning
Guillaume Bellegarda, Katie Byl
Recent breakthroughs in the reinforcement learning (RL) community have made significant advances towards learning and deploying policies on real world robotic systems. However, eve…
Mesh-based Tools to Analyze Deep Reinforcement Learning Policies for Underactuated Biped Locomotion
Nihar Talele, Katie Byl
In this paper, we present a mesh-based approach to analyze stability and robustness of the policies obtained via deep reinforcement learning for various biped gaits of a five-link…
First Passage Value
Cenk Oguz Saglam, Katie Byl
For many stochastic dynamic systems, the Mean First Passage Time (MFPT) is a useful concept, which gives expected time before a state of interest. This work is an extension of MFPT…