PPT-Deep Reinforcement Learning: Q-Learning

Garima Lalwani Karan Ganju Unnat Jain Todays takeaways Bonus RL recap Functional Approximation Deep Q Network Double Deep Q Network Dueling Networks Recurrent DQN Solving Doom. 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. Module 2. 1. Topics . Introduction. Development. Design Methodology. IS and ACI provisions. Applications. Deep beams. Corbels. Beam-column joints. 2. Hydrostatic state of stress. Nodal zone dimensions proportional to the applied compressive forces. 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 . Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . 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.. 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. 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. 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. 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] CS 285 Deep Reinforcement Learning Decision Making and ControlSergey LevineClass Notes1Homework 4 due todayRecap whats the problemthis is easy mostlythis is impossibleWhyRecap classes of exploration m Deep Q-learning. Instructor: Guni Sharon. 1. CSCE-689, Reinforcement Learning. Stateless decision process. Markov decision process. Solving MDPs (offline). Dynamic programming . Monte-Carlo. Temporal difference.