Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search

In principle, reinforcement learning and policy search methods can enable robots to learn highly complex and general skills that may allow them to function amid the complexity and diversity of the real world. However, training a policy that generalizes well across a wide range of real-world conditions requires far greater quantity and diversity of experience than is practical to collect with a single robot. Fortunately, it is possible for multiple robots to share their experience with one another, and thereby, learn a policy collectively. In this work, we explore distributed and asynchronous policy learning as a means to achieve generalization and improved training times on challenging, real-world manipulation tasks. We propose a distributed and asynchronous version of Guided Policy Search and use it to demonstrate collective policy learning on a vision-based door opening task using four robots. We show that it achieves better generalization, utilization, and training times than the single robot alternative.Comment: Submitted to the IEEE International Conference on Robotics and Automation 201

Leaning an University Department: a life experiment

The European Quality Assurance methodology is pushing hard Portuguese Universities so that they should improve their overall performance. Working at a Portuguese University more than a decade ago, one of the authors experienced several life cycles in different Departments and the experience acquired in foreign Universities (USA) teached him a couple of simple things in order to positively participate in this kind of processes. However, he found it quite difficult to apply his knowledge without other’s contribution, due to several endogenous and exogenous reasons, including age and generation viewpoints. Together with the second author we started to apply some theoretical new insights we were discussing together during her PhD research. The purpose of this paper is to describe the experiment we are in now, following a social network methodology used in my Economics PhD together with three theoretical influences we think are inter twinkled like the lean thinking, the value focus thinking and the complication in innovation diffusion processes. After a brief literature review we describe the basic pillars we used to achieve the main goal of improving performance in a young university department. Using some coaching and economic tools and knowledge, we were able to gather a group of different people – students, staff and teachers - deeply involved in our proposal methodology. Preliminary results are briefly identified, as much as further research challenges.Lean thinking; quality improvement; social networks analysis; decision making; Portuguese Universities

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era