5 citations · 14 across the 8 of their papers we have counts for
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cs.RO2019★ 5 cited
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…
cs.RO2019
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…
cs.RO2019★ 1 cited
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…