Unit 1
Introduction to Reinforcement Learning – History of Reinforcement Learning – Elements of Reinforcement Learning – Limitations and scope.
Course Name | Reinforcement Learning |
Course Code | 24AIM304 |
Program | B.Tech. in Artificial Intelligence (AI) and Data Science (DS) in Medical Engineering |
Semester | V - Micro-credential courses: Set 4 |
Credits | 3 |
Campus | Coimbatore |
Introduction to Reinforcement Learning – History of Reinforcement Learning – Elements of Reinforcement Learning – Limitations and scope.
Multi-armed Bandits – Finite Markov Decision Processes – Dynamic Programming – Policy evaluation – Policy improvement – Policy Iteration – Value Iteration.
Monte Carlo Methods – Monte Carlo prediction – Monte Carlo control – Incremental Implementation – Temporal- Difference Learning – TD prediction – Q-Learning – n-step Bootstrapping.
Planning and Learning with Tabular Methods – Models and planning – Prioritized sweeping – Trajectory sampling – Heuristic search – Rollout algorithms.
Course Objectives:
Outcomes Course:
After completing this course, students should be able to
CO1: Demonstrate sound understanding of the foundations of Reinforcement Learning
CO2: Demonstrate proficiency in Multi-armed Bandits and Markov Decision Processes
CO3: Apply Monte Carlo Methods and Temporal-Difference Learning
CO4: Apply Tabular Methods in Planning and Learning
CO5: Employ Reinforcement Learning Concepts in Real-world Applications
CO-PO Mapping
CO/PO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | PSO3 |
CO1 | 3 | 3 | 1 | – | – | – | – | 2 | 2 | 2 | – | 2 | 2 | 2 | 2 |
CO2 | 3 | 3 | 1 | – | 2 | – | – | – | 2 | 2 | – | 2 | 2 | 2 | 2 |
CO3 | 3 | 3 | 1 | – | 2 | – | – | – | 2 | 2 | – | 2 | 2 | 2 | 2 |
CO4 | 3 | 3 | 1 | – | 2 | – | – | – | 2 | 2 | – | 2 | 2 | 2 | 2 |
CO5 | 3 | – | 1 | – | 2 | – | – | – | 2 | 2 | – | 2 | 2 | 2 | 2 |
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