PPT-Resource Management with Deep Reinforcement Learning

Hongzi Mao Mohammad Alizadeh Ishai Menache Srikanth Kandula Resource management is ubiquitous Cluster scheduling Video streaming Internet telephony Virtual machine placement. 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. Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . . 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.. 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. 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. . 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] 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. 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.