1 code implementation • 11 Oct 2024 • Jacob Levy, Tyler Westenbroek, David Fridovich-Keil
We leverage this capability and propose Semi-Structured Reinforcement Learning ($\texttt{SSRL}$) a simple model-based learning framework which pushes the sample complexity boundary for real-world learning.
Model-based Reinforcement Learning reinforcement-learning +1
1 code implementation • 16 Jul 2023 • Tyler Westenbroek, Jacob Levy, David Fridovich-Keil
We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data.
no code implementations • 16 May 2023 • Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu
A common pipeline in learning-based control is to iteratively estimate a model of system dynamics, and apply a trajectory optimization algorithm - e. g.~$\mathtt{iLQR}$ - on the learned model to minimize a target cost.
no code implementations • 5 Apr 2022 • Tyler Westenbroek, Anand Siththaranjan, Mohsin Sarwari, Claire J. Tomlin, Shankar S. Sastry
However, despite the extensive impacts of methods such as receding horizon control, dynamic programming and reinforcement learning, the design of cost functions for a particular system often remains a heuristic-driven process of trial and error.
no code implementations • 27 Mar 2021 • Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry
Crucially, this guarantee requires that state costs applied to the planning problems are in a certain sense `compatible' with the global geometry of the system, and a simple counter-example demonstrates the necessity of this condition.
no code implementations • L4DC 2020 • Fernando Castañeda, Mathias Wulfman, Ayush Agrawal, Tyler Westenbroek, Claire J. Tomlin, S. Shankar Sastry, Koushil Sreenath
The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints.
no code implementations • 6 Apr 2020 • Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry
This paper proposes a framework for adaptively learning a feedback linearization-based tracking controller for an unknown system using discrete-time model-free policy-gradient parameter update rules.
no code implementations • 29 Oct 2019 • Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin
We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics.
no code implementations • 29 Apr 2019 • Tyler Westenbroek, Roy Dong, Lillian J. Ratliff, S. Shankar Sastry
Recent work has explored mechanisms to ensure that the data sources share high quality data with a single data aggregator, addressing the issue of moral hazard.