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REINFORCEMENT LEARNING: AN INTRODUCTION - IMTR

REINFORCEMENT learning : AN INTRODUCTION Ianis Lallemand, 24 octobre 2012 This presentation is based largely on the book: REINFORCEMENT learning : An INTRODUCTION , Richard S. Sutton and Andrew G. Barto, MIT Press, Cambridge, MA, 1998 REINFORCEMENT learning : AN INTRODUCTION 1. General definition 2. Elements of REINFORCEMENT learning 3. Example 1: Tic-Tac-Toe 4. Example 2: n armed bandit 5. The full REINFORCEMENT learning problem 6. Dynamic programming 7. Monte Carlo Methods 8. Temporal Difference learningcontentsGENERAL DEFINITION general definition" REINFORCEMENT learning is learning what to do how to map situations to actions so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning , but instead must discover which actions yield the most reward by trying them."GENERAL DEFINITION general definition" REINFORCEMENT learning is learning what to do how to map situations to actions so as to maximize a numerical reward signal.

Reinforcement learning with tabular action-value function. Store in a table the current estimated values of each action. The true value of an action is the average reward received when this action

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