Transcription of Introduction of Reinforcement Learning - 國立臺灣大學
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Introduction of Reinforcement Learning - 國立臺灣大學
Introduction of Reinforcement Learning Deep Reinforcement Learning Reference Textbook: Reinforcement Learning : An Introduction Lectures of David Silver ml (10 lectures, around 1:30 each). t_learning/ (Deep Reinforcement Learning ). Lectures of John Schulman Scenario of Reinforcement Learning Observation Action State Change the environment Agent Don't do Reward that Environment Scenario of Reinforcement Learning Agent learns to take actions maximizing expected reward. Observation Action State Change the environment Agent Thank you. Reward Environment to-clean-site-structure/. Machine Learning Looking for a Function Actor/Policy Observation Action Action =. Function Function ( Observation ) output input Used to pick the Reward best function Environment Learning to play Go Observation Action Reward Next Move Environment Agent learns to take Learning to play Go actions maximizing expected reward. Observation Action Reward reward = 0 in most cases If win, reward = 1.
Scenario of Reinforcement Learning Agent Environment Observation Action Don’t do Reward that State Change the environment
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