1 citations · 1 across the 5 of their papers we have counts for
5 papers
Sim-Anchored Learning for On-the-Fly Adaptation
Bassel El Mabsout, Shahin Roozkhosh, Siddharth Mysore +2
Fine-tuning simulation-trained RL agents with real-world data often degrades crucial behaviors due to limited or skewed data distributions. We argue that designer priorities exist…
Designing Composites with Target Effective Young's Modulus using Reinforcement Learning
Aldair E. Gongora, Siddharth Mysore, Beichen Li +5
Advancements in additive manufacturing have enabled design and fabrication of materials and structures not previously realizable. In particular, the design space of composite mater…
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…
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…
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…