Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

TOKEN-BASED APPROACH FOR SCALABLE TEAMCOORDINATION

To form a cooperative multiagent team, autonomous agents are required to harmonize activities and make the best use of exclusive resources to achieve their common goal. In addition, to handle uncertainty and quickly respond to external environmental events, they should share knowledge and sensor in formation. Unlike small team coordination, agents in scalable team must limit the amount of their communications while maximizing team performance. Communication decisions are critical to scalable-team coordination because agents should target their communications, but these decisions cannot be supported by a precise model or by complete team knowledge.The hypothesis of my thesis is: local routing of tokens encapsulating discrete elements of control, based only on decentralized local probability decision models, will lead to efficient scalable coordination with several hundreds of agents. In my research, coordination controls including all domain knowledge, tasks and exclusive resources are encapsulated into tokens. By passing tokens around, agents transfer team controls encapsulated in the tokens. The team benefits when a token is passed to an agent who can make use of it, but communications incur costs. Hence, no single agent has sole responsible over any shared decision. The key problem lies in how agents make the correct decisions to target communications and pass tokens so that they will potentially benefit the team most when considering communication costs.My research on token-based coordination algorithm starts from the investigation of random walk of token movement. I found a little increase of the probabilities that agents make the right decision to pass a token, the overall efficiency of the token movement could be greatly enhanced. Moreover, if token movements are modeled as a Markov chain, I found that the efficiency of passing tokens could be significantly varied based on different network topologies.My token-based algorithm starts at the investigation of each single decision theoretic agents. Although under the uncertainties that exist in large multiagent teams, agents cannot act optimal, it is still feasible to build a probability model for each agents to rationally pass tokens. Specifically, this decision only allow agent to pass tokens over an associate network where only a few of team members are considered as token receiver.My proposed algorithm will build each agent's individual decision model based on all of its previously received tokens. This model will not require the complete knowledge of the team. The key idea is that I will make use of the domain relationships between pairs of coordination controls. Previously received tokens will help the receiver to infer whether the sender could benefit the team if a related token is received. Therefore, each token is used to improve the routing of other tokens, leading to a dramatic performance improvement when more tokens are added. By exploring the relationships between different types of coordination controls, an integrated coordination algorithm will be built, and an improvement of one aspect of coordination will enhance the performance of the others

From Activity Recognition to Intention Recognition for Assisted Living Within Smart Homes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The global population is aging; projections show that by 2050, more than 20% of the population will be aged over 64. This will lead to an increase in aging related illness, a decrease in informal support, and ultimately issues with providing care for these individuals. Assistive smart homes provide a promising solution to some of these issues. Nevertheless, they currently have issues hindering their adoption. To help address some of these issues, this study introduces a novel approach to implementing assistive smart homes. The devised approach is based upon an intention recognition mechanism incorporated into an intelligent agent architecture. This approach is detailed and evaluated. Evaluation was performed across three scenarios. Scenario 1 involved a web interface, focusing on testing the intention recognition mechanism. Scenarios 2 and 3 involved retrofitting a home with sensors and providing assistance with activities over a period of 3 months. The average accuracy for these three scenarios was 100%, 64.4%, and 83.3%, respectively. Future will extend and further evaluate this approach by implementing advanced sensor-filtering rules and evaluating more complex activities

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT