Reinforcement Learning

Indian Institute of Technology Madras and NPTEL via Swayam

Swayam based on 0 ratings

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Overview

Reinforcement Learning (RL) is an advanced machine learning technique that enables agents to learn optimal actions through trial and error by interacting with an environment. This course covers the core principles of RL, including value iteration, Q-learning, policy gradients, and deep reinforcement learning. Students will also explore various applications of RL, such as robotics, autonomous systems, and game playing. Using Python and popular libraries like Tensor Flow and Py Torch, learners will gain hands-on experience implementing RL algorithms and applying...

Syllabus

Week 1 : IntroductionWeek 2 : Bandit algorithms UCB, PACWeek 3: Bandit algorithms Median Elimination, Policy GradientWeek 4: Full RL & MDPsWeek 5 : Bellman OptimalityWeek 6: Dynamic Programming & TD MethodsWeek 7 : Eligibility TracesWeek 8 : Function ApproximationWeek 9: Least Squares MethodsWeek 10: Fitted Q, DQN & Policy Gradient for Full RLWeek 11: Hierarchical RLWeek 12: POMDPs