3,818 research outputs found
Reinforcement Learning using Augmented Neural Networks
Neural networks allow Q-learning reinforcement learning agents such as deep
Q-networks (DQN) to approximate complex mappings from state spaces to value
functions. However, this also brings drawbacks when compared to other function
approximators such as tile coding or their generalisations, radial basis
functions (RBF) because they introduce instability due to the side effect of
globalised updates present in neural networks. This instability does not even
vanish in neural networks that do not have any hidden layers. In this paper, we
show that simple modifications to the structure of the neural network can
improve stability of DQN learning when a multi-layer perceptron is used for
function approximation.Comment: 7 pages; two columns; 4 figure
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification
Convolutional Neural Networks (CNNs) have demonstrated their superiority in
image classification, and evolutionary computation (EC) methods have recently
been surging to automatically design the architectures of CNNs to save the
tedious work of manually designing CNNs. In this paper, a new hybrid
differential evolution (DE) algorithm with a newly added crossover operator is
proposed to evolve the architectures of CNNs of any lengths, which is named
DECNN. There are three new ideas in the proposed DECNN method. Firstly, an
existing effective encoding scheme is refined to cater for variable-length CNN
architectures; Secondly, the new mutation and crossover operators are developed
for variable-length DE to optimise the hyperparameters of CNNs; Finally, the
new second crossover is introduced to evolve the depth of the CNN
architectures. The proposed algorithm is tested on six widely-used benchmark
datasets and the results are compared to 12 state-of-the-art methods, which
shows the proposed method is vigorously competitive to the state-of-the-art
algorithms. Furthermore, the proposed method is also compared with a method
using particle swarm optimisation with a similar encoding strategy named IPPSO,
and the proposed DECNN outperforms IPPSO in terms of the accuracy.Comment: Accepted by The Australasian Joint Conference on Artificial
Intelligence 201
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