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Unit-I
Introduction and Foundations of Reinforcement Learning: Historical perspective of Reinforcement Learning (RL). Basics of RL: definition, Motivation, examples, terminology, notation, assumptions, and applications; Comparison with supervised and unsupervised learning; Elements of Reinforcement Learning – agent, environment, state, action, reward; Reward hypothesis. Markov property: Markov Decision Processes (MDPs): states, actions, transition probabilities, reward functions, discount factor; Return and value functions. Policies – deterministic and stochastic; State-value function and action-value function; Bellman expectation equations; Optimal value functions and optimal policies. Multi-arm Bandits, Contextual Bandits and MDP Extensions [8]
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Reinforcement Learning (AIML401) is a semester 7 subject in the IPU B.Tech AI & ML (Artificial Intelligence and Machine Learning) (AI-ML) curriculum.
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