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A Brief Introduction to Reinforcement Learning

A Brief Introduction to Reinforcement LearningJingwei Zhang Characteristics of Reinforcement Learning (RL) Components of RL (MDP, value, policy, Bellman) Planning (policy iteration, value iteration) Model-free Prediction (MC, TD) Model-free Control (Q- Learning ) Deep Reinforcement Learning (DQN)2 Outline Characteristics of Reinforcement Learning (RL) The RL Problem (MDP, value, policy, Bellman) Planning (policy iteration, value iteration) Model-free Prediction (MC, TD) Model-free Control (Q- Learning ) Deep Reinforcement Learning (DQN)4 Characteristics of RL SL VS RL Supervised Learning data direct and strong supervision (label: what is the right thing to do) instantaneous feedback Reinforcement Learning sequential data, no supervisor, only a reward signal (rule: what you did is good or bad) delayed feedback5 Characteristics of RL SL VS RL Su

A Brief Introduction to Reinforcement Learning Jingwei Zhang zhang@informatik.uni-freiburg.de 1

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