Programme Parameters
Graduate / Intermediate
4 Weeks (40 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
Reinforcement Learning takes you from the core idea — an agent learning by trial, reward and interaction — to working deep-RL systems. You will formalise problems as Markov decision processes, implement value-based methods like Q-learning and DQN, and move on to policy-gradient and actor-critic approaches. Alongside the algorithms you will build the intuition that matters in practice: shaping rewards, balancing exploration against exploitation, and diagnosing why an agent fails to converge. Hands-on environments let you train and evaluate your own agents throughout. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course teaches reinforcement learning from Markov decision processes and Q-learning through policy-gradient and deep RL, with agents you build and train yourself.
📋 Course Objectives
1. Formalise decision problems as Markov decision processes.
2. Implement value-based methods including Q-learning and DQN.
3. Build policy-gradient and actor-critic agents.
4. Balance exploration and exploitation and shape rewards effectively.
5. Train and evaluate agents in simulated environments.
👥 Who Should Enroll?
• ML practitioners extending into sequential decision-making
• PhD scholars and researchers in AI and control
• Engineers building agents for robotics, games or operations
• Data scientists moving beyond supervised learning
🚀 Key Learning Outcomes
• A working RL agent you have trained and tuned yourself.
• Clear intuition for when and why RL methods succeed or fail.
• A project demonstrating deep reinforcement learning.
• 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
AI Fundamentals, Mathematics, and Reinforcement Learning Foundations
Apply linear algebra and calculus concepts to solve reinforcement learning problems • Derive and implement Bellman equations to model Markov decision processes • Design and analyze simple reinforcement learning algorithms using Python and NumPy
Data Engineering, Preprocessing, and Feature Pipelines
Configure and manage large datasets for reinforcement learning using Apache Spark and Hadoop • Develop and evaluate data preprocessing pipelines using scikit-learn and pandas • Implement feature engineering techniques to extract relevant information from raw data
Model Architecture, Algorithm Design, and Reinforcement Learning Methods
Design and implement deep neural networks for reinforcement learning using TensorFlow and Keras • Evaluate and compare different reinforcement learning algorithms such as Q-learning and SARSA • Develop and analyze model architectures for complex reinforcement learning tasks
Training, Hyperparameter Optimization, and Evaluation
Train and optimize reinforcement learning models using gradient-based methods and evolutionary algorithms • Implement and evaluate hyperparameter tuning techniques using grid search and random search • Analyze and visualize reinforcement learning model performance using metrics such as cumulative reward and episode length
Deployment, MLOps, and Production Workflows
Deploy reinforcement learning models in production environments using Docker and Kubernetes • Develop and implement MLOps pipelines for continuous integration and deployment • Configure and manage model serving and monitoring systems using TensorFlow Serving and Prometheus
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and mitigate bias in reinforcement learning models using fairness metrics and debiasing techniques • Develop and implement responsible AI practices for transparency, accountability, and explainability • Evaluate and compare different ethics frameworks for AI development and deployment
Industry Integration, Business Applications, and Case Studies
Apply reinforcement learning to real-world business problems such as robotics and autonomous systems • Develop and evaluate reinforcement learning solutions for industry-specific challenges such as supply chain optimization • Analyze and discuss case studies of successful reinforcement learning deployments in various industries
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | pandas |
| Covered Tool / Platform | NumPy |
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