161,202 research outputs found
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
We present a tutorial on Bayesian optimization, a method of finding the
maximum of expensive cost functions. Bayesian optimization employs the Bayesian
technique of setting a prior over the objective function and combining it with
evidence to get a posterior function. This permits a utility-based selection of
the next observation to make on the objective function, which must take into
account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling areas likely to offer improvement over the current best
observation). We also present two detailed extensions of Bayesian optimization,
with experiments---active user modelling with preferences, and hierarchical
reinforcement learning---and a discussion of the pros and cons of Bayesian
optimization based on our experiences
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy
This paper presents a novel mechanism to adapt surrogate-assisted
population-based algorithms. This mechanism is applied to ACM-ES, a recently
proposed surrogate-assisted variant of CMA-ES. The resulting algorithm,
saACM-ES, adjusts online the lifelength of the current surrogate model (the
number of CMA-ES generations before learning a new surrogate) and the surrogate
hyper-parameters. Both heuristics significantly improve the quality of the
surrogate model, yielding a significant speed-up of saACM-ES compared to the
ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the
BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability
w.r.t the problem dimension and the population size of the proposed approach,
that reaches new best results on some of the benchmark problems.Comment: Genetic and Evolutionary Computation Conference (GECCO 2012) (2012
Reactive Planar Manipulation with Convex Hybrid MPC
This paper presents a reactive controller for planar manipulation tasks that
leverages machine learning to achieve real-time performance. The approach is
based on a Model Predictive Control (MPC) formulation, where the goal is to
find an optimal sequence of robot motions to achieve a desired object motion.
Due to the multiple contact modes associated with frictional interactions, the
resulting optimization program suffers from combinatorial complexity when
tasked with determining the optimal sequence of modes.
To overcome this difficulty, we formulate the search for the optimal mode
sequences offline, separately from the search for optimal control inputs
online. Using tools from machine learning, this leads to a convex hybrid MPC
program that can be solved in real-time. We validate our algorithm on a planar
manipulation experimental setup where results show that the convex hybrid MPC
formulation with learned modes achieves good closed-loop performance on a
trajectory tracking problem
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