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Bonus Lecture: Introduction to Reinforcement Learning

Bonus lecture : Introduction to Reinforcement Learning Garima Lalwani, Karan Ganju and Unnat Jain Credits: These slides and images are borrowed from slides by David Silver and Peter Abbeel Outline 1 RL Problem Formulation 2 Model-based Prediction and Control 3 Model-free Prediction 4 Model-free Control 5 Summary Part 1: RL Problem Formulation Characteristics of Reinforcement Learning What makes Reinforcement Learning different from other machine Learning paradigms? There is no supervisor, only a reward signal Feedback is delayed, not instantaneous Time really matters (correlated, non data). Agent's actions affect the subsequent data it receives Agent and Environment Agent Observed state action St At reward Rt Environment Rewards A reward Rt is a scalar feedback signal Indicates how well agent is doing at step t The agent's job is to maximise cumulative reward Rod Balancing Demo Learn to swing up and

Bonus Lecture: Introduction to Reinforcement Learning Garima Lalwani, Karan Ganju and Unnat Jain Credits: These slides and images are borrowed from slides by David Silver and Peter Abbeel

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