A Method for Embodied Co-Learning in Interdependent Human-Robot Teams

This paper addresses the research question: “How can a human-robot team achieve co-learning, and interdependence in physically embodied tasks?”<br/>A method has been developed that enables a human-robot team to co-learn the handover of an object from the robot to the human. Five design requirements were composed to address the challenges of...

The Feasibility of Reinforcement Learning as a replacement for Mixed-Integer Programming for Smart Charging of Electric Vehicles

As the world is currently actively trying to reduce the consumption of fossil fuels, large investments are done in renewable energy sources and ways are sought after to electrify fossil fuel-intensive sectors. In line with these developments, the number of electric vehicles requiring access to the electric power grid has exploded putting...

Simulating an Scheduling Airport Security Checkpoints: Q-Learning-Based Allocation of Operators to Security Teams at an Airport Security Checkpoint

In this paper, we propose and analyze a q-learning-based approach for allocation of operators to security teams in order to improve operational efficiency of an airport security checkpoint. The research is composed of two parts. First, we develop an agent-based model capable of simulating an airport security checkpoint. Second, we introduce...

Comparing Model-Free Deep Reinforcement Learning Algorithms on Stock Market

Automated asset trading is a crucial method used by financial entities such as investment firms or hedge funds. It allows them to allocate their capital in order to maximize their rate of returns. In scientific literature, there are multiple models suggested to solve this problem. However, these models either lack the complexity to understand...

Comparing Deep Reinforcement Learning Approaches for Sparse Reward Settings with Discrete State-Action Spaces

One of the most challenging types of environments for a Deep Reinforcement Learning agent to learn in are those with sparse reward functions. There exist algorithms that are designed to perform well in settings with sparse rewards, but they are often applied to continuous state-action spaces, since economically relevant problems like robotic...

Safe Curriculum Learning for Linear Systems With Unknown Dynamics in Primary Flight Control

Safe Curriculum Learning constitutes a collection of methods that aim at enabling Rein- forcement Learning (RL) algorithms on complex systems and tasks whilst considering the safety and efficiency aspect of the learning process. On the one hand, curricular reinforce- ment learning approaches divide the task into more gradual complexity stages to...

Reinforcement Learning for Profiled Side-Channel Analysis: Applications of Q-Learning in the SCA Domain

Side-channel attacks (SCA), which use unintended leakage to retrieve a secret cryptographic key, have become more sophisticated over time. With the recent successes of machine learning (ML) and especially deep learning (DL) techniques against cryptographic implementations even in the presence of dedicated countermeasures, various methods have...

Reinforcement Learning for minimizing the total waiting time of passengers in a Taxi Dispatch Problem

A Taxi Dispatch Problem involves assigning taxis to requests of passengers who are waiting at different locations for a trip. In today's economy and society, the Taxi Dispatch Problem and other transport problems can be found everywhere. Not only in transporting people, but also in food delivery from restaurants and package delivery for all kind...

Obstacle avoidance for quadrotors using reinforcement learning and obstacle-airflow interactions

This research investigates and proposes a new method for obstacle detection and avoidance on quadrotors. One that does not require the addition of any sensors, but relies solely on measurements from the accelerometer and rotor controllers. The detection of obstacles is based on the principle that the airflow around a quadrotor changes when the...

Limit order placement optimization with Deep Reinforcement Learning: Learning from patterns in cryptocurrency market data

For various reasons, financial institutions often make use of high-level trading strategies when buying and selling assets. Many individuals, irrespective or their level of prior trading knowledge, have recently entered the field of trading due to the increasing popularity of cryptocurrencies, which offer a low entry barrier for trading....

Vision-Based Reinforcement Learning for the guidance of an AR Drone 2

Reinforcement Learning (RL) has been applied to teach quadcopters guidance tasks. Most applications rely on position information from an absolute reference<br/>system such as Global Positioning System (GPS). The dependence on "absolute<br/>position" information is a general limitation in the autonomous flight of Unmanned Aerial Vehicles (UAVs)....

Continuous state and action Q-learning framework applied to quadrotor UAV control

This paper describes an implementation of a reinforcement learning-based framework applied to the control of a multi-copter rotorcraft. The controller is based on continuous state and action Q-learning. The policy is stored using a radial basis function neural network. Distance-based neuron activation is used to optimize the generalization...

Reinforcement Learning Across Timescales

This project addresses a fundamental problem faced by many reinforcement learning agents. Commonly used reinforcement learning agents can be seen to have deteriorating performances at increasing frequencies, as they are unable to correctly learn the ordering of expected returns for actions that are applied. We call this the disappearing...

Potential Field Methods for Safe Reinforcement Learning: Exploring Q-Learning and Potential Fields

A Reinforcement Learning (RL) agent learns about its environment through exploration. For most physical applications such as search and rescue UAVs, this exploration must take place with safety in mind. Unregulated exploration, especially at the beginning of a run, will lead to fatal situations such as crashes. One approach to mitigating these...