Self-adaptive node-based PCA encodings

In this paper we propose an algorithm, Simple Hebbian PCA, and prove that it is able to calculate the principal component analysis (PCA) in a distributed fashion across nodes. It simplifies existing network structures by removing intralayer weights, essentially cutting the number of weights that need to be trained in half

Pseudorehearsal in actor-critic agents with neural network function approximation

Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found that pseudorehearsal can assist learning and decrease forgetting

Pseudorehearsal in actor-critic agents with neural network function approximation

Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found that pseudorehearsal can assist learning and decrease forgetting

Pseudorehearsal in actor-critic agents with neural network function approximation

Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found that pseudorehearsal can assist learning and decrease forgetting

SOT: First release

<p>Version used in paper to be published approximately recreated from recovered version.</p

Self-organizing trajectories

Abstract Trajectories and parameterized curves are data types of growing importance. Many measures for such data have been proposed in order to provide analogues to the mean and variance of vectors. We identify a counterintuitive oscillating behaviour of dynamic time warp-based averages on certain data sets. We present an algorithm that combines ideas from from both self-organizing maps and dynamic time warping that avoids these oscillations and hence promises more representative curve averages. These improvements also allow for accurate estimation of the piece-wise variance for a set of general N-dimensional trajectories. The run-time performance is demonstrated on movement data from rowing, where we are able to provide performance feedback in real-time to users in a simulator