Simulation optimization (SO) refers to the optimization of an objective function subject to
constraints, both of which can be evaluated through a stochastic simulation. To address …

Different fields have been thriving with the advents in mobile communication systems in
recent years. These fields reap benefits of data collected by Internet of Things (IoT) in next …

In human environments, robots are expected to accomplish a variety of manipulation tasks
given simple natural language instructions. Yet, robotic manipulation is extremely …

A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …

We tackle the problem of learning complex, general behaviors directly in the real world. We
propose an approach for robots to efficiently learn manipulation skills using only a handful of …

Policy search is a subfield in reinforcement learning which focuses on finding good
parameters for a given policy parametrization. It is well suited for robotics as it can cope with …

Policy-gradient-based actor-critic algorithms are amongst the most popular algorithms in the
reinforcement learning framework. Their advantage of being able to search for optimal …

Reinforcement learning is a learning paradigm concerned with learning to control a system
so as to maximize a numerical performance measure that expresses a long-term objective …

This paper introduces a machine learning based system for controlling a robotic manipulator
with visual perception only. The capability to autonomously learn robot controllers solely …