18 citations · 35 across the 13 of their papers we have counts for
3 papers · 1 filter
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations
Cevahir Koprulu, Po-han Li, Tianyu Qiu +5
Many continuous control problems can be formulated as sparse-reward reinforcement learning (RL) tasks. In principle, online RL methods can automatically explore the state space to…
Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models
Tyler Westenbroek, Jacob Levy, David Fridovich-Keil
We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data. In recent years, policy gradient methods have emerged as a pro…
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek +2
A common pipeline in learning-based control is to iteratively estimate a model of system dynamics, and apply a trajectory optimization algorithm - e.g.~ - on the lea…