3 citations · 4 across the 4 of their papers we have counts for
5 papers
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…