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Subject Syllabus

AD-802 (B) - Reinforcement Learning

AIDS · Semester 8

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

Decision Process Polices Value Functions and Bellman Equations Explorations Exploitation Inside an RL agent Problems with reinforcement learning.

MDPs Bellman Optimality Dynamic Programming - Value iteration Policy iteration Policy Evaluation Value Iteration Policy Iteration DP Extensions and Convergence using Contraction Mapping Monte-Carlo (MC) Learning Temporal-Difference (TD) Learning TD-Lambda and Eligibility Traces.

Eligibility Traces Function Approximation Least Squares Methods Incremental Methods and Batch Methods Deep Q-Learning analysis Deep Q-Networks and Experience Replay.

policies by imitating optimal controllers DQN & Policy Gradient Policy Gradient Algorithms for Full RL, Hierarchical RL, POMDPs.

Inverse Reinforcement Learning Generative Adversarial Imitation Learning Recent Trends in RL Architectures.Deep Learning An MIT Press book Ian Goodfellow and YoshuaBengio and Aaron Courville 2.Pattern Classification- Richard O.Duda Peter E.Hart David G.Stork John Wiley & Sons Inc. 3.Reinforcement Learning: An Introduction Sutton and Barto, 2nd Edition. 4.Reinforcement Learning: State-of-the-Art Marco Wiering and Martijn van Otterlo Eds