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Found 11 papers, 3 papers with code
Computing the Newton-step faster than Hessian accumulation
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.
Lyceum: An efficient and scalable ecosystem for robot learning
We introduce Lyceum, a high-performance computational ecosystem for robot learning.
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning.
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
Reinforcement learning has emerged as a promising methodology for training robot controllers.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency.
Towards Generalization and Simplicity in Continuous Control
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.
Learning Dexterous Manipulation Policies from Experience and Imitation
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.
Universal Convexification via Risk-Aversion
We develop a framework for convexifying a fairly general class of optimization problems.
MuJoCo: A physics engine for model-based control
To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel.
Policy gradients in linearly-solvable MDPs
We present policy gradient results within the framework of linearly-solvable MDPs.
Compositionality of optimal control laws
We present a theory of compositionality in stochastic optimal control, showing how task-optimal controllers can be constructed from certain primitives.
