PPT-Reinforcement learning Few famous algorithms and applications

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 Qlearning. University of Wisconsin – Madison. HAMLET 2009. Reinforcement Learning. Reinforcement learning. What is it and why is it important in machine learning?. What machine learning algorithms exist for it?. 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. mwahahahahaha. Reinforcement. Any object or event that strengthens or . increases. the frequency of a response that it follows.. Punishment. Is the delivery of an unpleasant consequence following a response which . 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 . 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.. Alice F. Short. Hilliard Davidson High School. Chapter Preview. Classical Conditioning. Operant Conditioning. Observational Learning. Factors That Affect Learning. Learning and Health and Wellness. Types of Learning. Overview. Introduction to Reinforcement Learning. Finite Markov Decision Processes. Temporal-Difference Learning (SARSA, Q-learning, Deep Q-Networks) . Policy Gradient Methods (Finite . D. ifference Policy Gradient, REINFORCE, Actor-Critic). 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. 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”. . 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. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand