PPT-Reinforcement Learning

Overview Introduction Qlearning Exploration Exploitation Evaluating RL algorithms OnPolicy learning SARSA Modelbased Qlearning What Does QLearning learn Does Qlearning gives the agent an optimal policy . Goal .  How do we learn behaviors through . classical conditioning. ?. Learning is…. Relatively permanent. Change in behavior. Due to experience. Behaviorism. .  Psychology . should focus on observable . 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. 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. 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. Associative Learning. 3. Learning to associate one stimulus. with another.. CONDITIONING = LEARNING. Classical Conditioning. Meat Powder. Salivation. Meat Powder. Salivation. Tone. Salivation. Tone. Classical Conditioning. 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). 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. 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 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)..