PPT-Deep Reinforcement Learning

Deep Reinforcement Learning Sanket Lokegaonkar Advanced Computer Vision ECE 6554 Outline The Why Gliding Over All An Introduction Classical RL DQNEra Playing Atari with Deep Reinforcement Learning 2013. 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. 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. 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. 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 . 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. 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. 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”. . 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.