no code implementations • NeurIPS 2009 • Emanuel Todorov
We present a theory of compositionality in stochastic optimal control, showing how task-optimal controllers can be constructed from certain primitives.
no code implementations • NeurIPS 2010 • Emanuel Todorov
We present policy gradient results within the framework of linearly-solvable MDPs.
1 code implementation • IEEE/RSJ IROS 2012 • Emanuel Todorov, Tom Erez, Yuval Tassa
To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel.
no code implementations • 3 Jun 2014 • Krishnamurthy Dvijotham, Maryam Fazel, Emanuel Todorov
We develop a framework for convexifying a fairly general class of optimization problems.
no code implementations • 15 Nov 2016 • Vikash Kumar, Abhishek Gupta, Emanuel Todorov, Sergey Levine
We demonstrate that such controllers can perform the task robustly, both in simulation and on the physical platform, for a limited range of initial conditions around the trained starting state.
1 code implementation • NeurIPS 2017 • Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham Kakade
This work shows that policies with simple linear and RBF parameterizations can be trained to solve a variety of continuous control tasks, including the OpenAI gym benchmarks.
1 code implementation • 28 Sep 2017 • Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, Sergey Levine
Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency.
no code implementations • 28 Mar 2018 • Kendall Lowrey, Svetoslav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov
Reinforcement learning has emerged as a promising methodology for training robot controllers.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • ICLR 2019 • Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, Igor Mordatch
We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning.
no code implementations • L4DC 2020 • Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha Srinivasa, Emanuel Todorov
We introduce Lyceum, a high-performance computational ecosystem for robot learning.
no code implementations • 2 Aug 2021 • Akshay Srinivasan, Emanuel Todorov
In this paper, we show that given the computational graph of the function, this bound can be reduced to $O(m\tau^3)$, where $\tau, m$ are the width and size of a tree-decomposition of the graph.