PPT-Reinforcement Learning Agenda

Online learning Reinforcement learning Modelfree vs modelbased Passive vs active learning Explorationexploitation tradeoff Reinforcement Learning RL problem given only observations of actions states and rewards learn a near optimal policy. 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. How to teach your child new skills to improve independence with ADL’s, chores and homework. Presented by . Sheila Guiney, M.Ed.. Northshore Education . Consortium. November 2015. Teaching your child new skills. 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. . 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.. 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.. 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. Changes can’t be explained by . Native response tendencies. Maturation, or . Temporary states (e.g. fatigue, drugs, etc). How do we learn?. Associative learning. – learning certain events occur together. Kretov. Maksim. 5. vision. 1 November 2015. Plan. Part A: Reminders. Key definitions of RL and MDP. Bellman equations. General structure of RL . tasks. Part B: Application to Atari . games. Q-learning. 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?. 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. . Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”.