5,137 research outputs found
Two neural network algorithms for designing optimal terminal controllers with open final time
Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results
Robust Multi-Cellular Developmental Design
This paper introduces a continuous model for Multi-cellular Developmental
Design. The cells are fixed on a 2D grid and exchange "chemicals" with their
neighbors during the growth process. The quantity of chemicals that a cell
produces, as well as the differentiation value of the cell in the phenotype,
are controlled by a Neural Network (the genotype) that takes as inputs the
chemicals produced by the neighboring cells at the previous time step. In the
proposed model, the number of iterations of the growth process is not
pre-determined, but emerges during evolution: only organisms for which the
growth process stabilizes give a phenotype (the stable state), others are
declared nonviable. The optimization of the controller is done using the NEAT
algorithm, that optimizes both the topology and the weights of the Neural
Networks. Though each cell only receives local information from its neighbors,
the experimental results of the proposed approach on the 'flags' problems (the
phenotype must match a given 2D pattern) are almost as good as those of a
direct regression approach using the same model with global information.
Moreover, the resulting multi-cellular organisms exhibit almost perfect
self-healing characteristics
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Simulations are attractive environments for training agents as they provide
an abundant source of data and alleviate certain safety concerns during the
training process. But the behaviours developed by agents in simulation are
often specific to the characteristics of the simulator. Due to modeling error,
strategies that are successful in simulation may not transfer to their real
world counterparts. In this paper, we demonstrate a simple method to bridge
this "reality gap". By randomizing the dynamics of the simulator during
training, we are able to develop policies that are capable of adapting to very
different dynamics, including ones that differ significantly from the dynamics
on which the policies were trained. This adaptivity enables the policies to
generalize to the dynamics of the real world without any training on the
physical system. Our approach is demonstrated on an object pushing task using a
robotic arm. Despite being trained exclusively in simulation, our policies are
able to maintain a similar level of performance when deployed on a real robot,
reliably moving an object to a desired location from random initial
configurations. We explore the impact of various design decisions and show that
the resulting policies are robust to significant calibration error
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
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