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

Real-time error detection for a rowing training system

The use of coaches in training provides several advantages to the skill transfer process. However, their use is limited because of cost reasons. Automatic recognition processes, on the other hand, have a vastly lower cost ratio and can replace or integrate many of the functions otherwise performed by a human. The paper presents an effective design of a real-time error recognition system for rowing in a simulator. It was designed in order to be easy to develop, be computationally efficient and offer an excellent classification ability