Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This course provides an in-depth understanding of Reinforcement Learning (RL), one of the most dynamic fields in Artificial Intelligence. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course provides an in-depth understanding of Reinforcement Learning (RL), one of the most dynamic fields in Artificial Intelligence.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Modelling a Problem as an MDP
• States, actions, rewards, transitions and the discount factor
• Reward specification and the reward hacking that follows a careless definition
• Why most real problems are partially observed rather than clean MDPs
Value and Policy Methods
• Dynamic programming, Monte Carlo and temporal difference learning
• Q-learning and SARSA, and the exploration-exploitation trade-off
• Function approximation and the instability it introduces
Scaling to Large State Spaces
• DQN with replay buffers and target networks, and why both are needed
• Policy gradients, PPO and actor-critic architectures
• Sample inefficiency as the dominant practical obstacle
Training Without Breaking Things
• Building a simulator with Gymnasium and validating that it matches reality
• The sim-to-real gap and domain randomisation as a partial answer
• Offline RL when only logged data is available
Applications and Their Constraints
• Robotics, control, recommendation and operations as the realistic domains
• Safe RL: constraints, shielding and fallback to a known-good policy
• Monitoring a deployed policy and detecting distribution shift
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
Frequently Asked Questions
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