PPT-Reinforcement Learning and Tetris

Jared Christen Tetris Markov decision processes Large state space Longterm strategy without longterm knowledge Background Handcoded algorithms can clear gt 1000000 lines Genetic algorithm by Roger . 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. 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.. 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.. 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. 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. 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 . 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. 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. 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) . The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Learning (7-9%) . AP students in psychology should be able to do the following. :. • Distinguish general differences between principles of classical conditioning, operant conditioning, and observational learning (e.g., contingencies)..