papers
Publications (3)
cs.RO2021
Regularizing Action Policies for Smooth Control with Reinforcement Learning
Siddharth Mysore, Bassel Mabsout, Renato Mancuso +1
A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of smoothness in the actions learned by the RL polici…
cs.RO2021
How to Train your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning
Siddharth Mysore, Bassel Mabsout, Kate Saenko +1
We focus on the problem of reliably training Reinforcement Learning (RL) models (agents) for stable low-level control in embedded systems and test our methods on a high-performance…
cs.LG2021
Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL
Siddharth Mysore, Bassel Mabsout, Renato Mancuso +1
Actors and critics in actor-critic reinforcement learning algorithms are functionally separate, yet they often use the same network architectures. This case study explores the perf…