PPT-Reinforcement Learning Reinforcement Learning: An Introduction

Humanlevel 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. Objective. Explain What is the Reinforcement Theory of Motivation. Explain What is meant by the ‘Law of Effect’. Explain What is meant by the ‘Quantitative Law of Effect’. Explain the Types of Reinforcement. Hector Munoz-Avila. Stephen Lee-Urban. www.cse.lehigh.edu/~munoz/InSyTe. Outline. Introduction. Adaptive Game AI. Domination games in Unreal Tournament©. Reinforcement Learning. Adaptive Game AI with Reinforcement Learning. Case Study:. . The Little Albert Experiment. Section 1:. . Classical Conditioning. Section 2:. . Operant Conditioning. Section 3:. . Cognitive Factors in Learning. Section 4:. . The PQ4R Method: Learning to Learn. Jared Christen. Tetris. Markov decision processes. Large state space. Long-term strategy without long-term knowledge. Background. Hand-coded algorithms can clear > 1,000,000 lines. Genetic algorithm by Roger . History. Established by Joseph . Klapper. (1960). Released a book ‘The Effects of Mass Communication’. Suggested that the media has little power to influence people. Thought it was important to move away from thinking that the media is all powerful in influence. . can be defined as the process leading to relatively permanent behavioral change or potential behavioral change. . Classical Conditioning. Ivan Pavlov’s . method of conditioning in which associations are made between a natural stimulus and a learned, neutral stimulus.. Aaron Schumacher. Data Science DC. 2017-11-14. Aaron Schumacher. planspace.org has these slides. Plan. applications. : . what. t. heory. applications. : . how. onward. a. pplications: what. Backgammon. optimisation. Milica. Ga. š. i. ć. Dialogue Systems Group. Structure of spoken . dialogue systems. Language understanding. Language generation. semantics. a. ctions. 2. Speech recognition. Dialogue management. SWOT Analysis. Strengths . Weaknesses. Appealing, well-designed stores. Fun, hip advertising. Quality merchandise. Helpful associates. Effective. p. romotional events. Higher prices than some competitors. With classical conditioning you can teach a dog to salivate, but you cannot teach it to sit up or roll over. Why?. Salivation is an involuntary reflex, while sitting up and rolling over are far more complex responses that we think of as voluntary. . Deep Reinforcement Learning Sanket Lokegaonkar Advanced Computer Vision (ECE 6554) Outline The Why? Gliding Over All : An Introduction Classical RL DQN-Era Playing Atari with Deep Reinforcement Learning [2013] A relatively permanent change in an organism’s behavior due to experience. Classical Conditioning. Operant Conditioning. Observational Learning. Classical Conditioning. Ivan Pavlov. Dogs trained to salivate (CR) to sound of bell (CS) . Quadrotor. Helicopters. Learning Objectives. Understand the fundamentals of . quadcopters. Quadcopter. control using reinforcement learning. Why . Quadcopters. ?. It can be used in various applications.. Chapter 5 Section 1. Introduction. Do your muscles tighten at the sound of a dentist’s drill?. Do you suddenly begin to salivate when passing your favorite restaurant?. You weren’t born with these responses- you learned them.