PPT-Reinforcement Learning Karan Kathpalia

Overview Introduction to Reinforcement Learning Finite Markov Decision Processes TemporalDifference Learning SARSA Qlearning Deep QNetworks Policy Gradient Methods Finite D ifference Policy Gradient REINFORCE ActorCritic. 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. 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.. 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. 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. . What is . a main . assumption of the behaviourist approach?. What is conditioning?. What type of conditioning was investigated by John Watson and . Rayner?. Who were the participants in . P. avlov’s research?. 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] . 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.