PDF-Autonomous Blimp Control using Modelfree Reinforcement

In contrast to previous approaches our method does not require sophisticated hand tuned models but rather learns the policy online which makes the system easily adaptable to changing conditions The blimp we apply our approach to is a smallscale vehi. 17th Street D Costa Mesa CA 92627 800 625 4677 949 650 1183 Fax 949 650 8421 wwwcaliforniablimpscom brPage 2br NOTICE WARNING California Blimps takes no responsibility for lost or damaged blimps because of failure to follow these instructions The F Miniature airships which are an instance of such robots are especially challenging since they behave nonlinearly typically are underactuated and also are subject to drift These aspects paired with their highdimensional state space demand ef64257cien Paata. J. Kervalishvili. 2. nd. SENS-ERA Workshop on “Advanced Sensor Systems and Networks” . TEI Piraeus, Athens, Greece December 6, 2012. These works are performed in close cooperation . with US colleagues lead by Prof. Alex Wiglinsky . Chris Schwarz. National Advanced Driving Simulator. Acknowledgements. Mid-America Transportation Center. 1 year project to survey literature and report on state of the art in autonomous vehicles. Co-PI: Prof. . (. Mobile Aerial Security System. ). Group 6. Derrick . Shrock. Henry . Chan. Eric . Hernandez . Sanjay . Yerra. 1. Motivation. Experience with Aviation work.. Extra . protection at public . events.. Lecture 4.3 :. Kinematics and Dynamics. Jürgen . Sturm. Technische. . Universität. . München. Kinematics. Describes . the motion of rigid bodies. Position. Velocity. Acceleration. Jürgen Sturm. Core:. Developed a motion planner for on-road swerve . maneuvers. Developed a reinforcement learning (RL) formulation that learns human driving . patterns . in simulation . playback. Recorded human driving . Lisa Morgan & Sara Shields. Roles and . Goals of officers. What is your role as a probation . or parole officer. ?. Agent of change or compliance monitor?. Roles and Goals. Compliance in conjunction with change. Human-level control through deep . reinforcment. learning. Dueling Network Architectures for Deep Reinforcement Learning. Reinforcement Learning. Reinforcement learning is a computational approach to understanding and automating good directed learning and decision making. It learns by interacting with the environment.. Machine ethics. AV. Can a machine “decide” anything?. 2. If a small tree branch pokes out onto a highway and there’s no incoming traffic, we’d simply drift a little into the opposite lane and drive around it. But an automated car might come to a full stop, as it dutifully observes traffic laws that prohibit crossing a double-yellow line. This unexpected move would avoid bumping the object in front, but then cause a crash with the human drivers behind it. Differential Schedules. Also called . Differentiation or IRT . schedules. .. Usually used with reinforcement . Used where the reinforcer depends BOTH on time and . the . number of reinforcers.. Provides . SWOT Analysis. Strengths . Weaknesses. Appealing, well-designed stores. Fun, hip advertising. Quality merchandise. Helpful associates. Effective. p. romotional events. Higher prices than some competitors. Risk Management. Probability. of Occurrence. High. Medium. Low. Low. Medium. High. Magnitude. of Impact. Module 6, Activity 1, Slide . 1. © SHRM. Module 6 Reinforcement Activity. Risk Management. The vice president of HR for a mid-sized bank has listed. Equal Pay Cases. Case 1: A tenured female associate professor in the industrial technology department is employed at a salary lower than male colleagues who are the same rank and teach similar courses at the same location. She is the second-lowest-paid professor in a department of close to 20, despite the fact that she has a higher rank and more seniority than four male colleagues. Does the scenario violate the Equal Pay Act?.