Reinforcement Learning for Real-World Applications

This workshop provides an in-depth understanding of Reinforcement Learning (RL), one of the most dynamic fields in Artificial Intelligence. Participants will explore foundational concepts, advanced techniques, and hands-on projects that demonstrate the application of RL in solving practical problems. From robotics to finance, this program covers how RL can optimize decision-making and automate complex systems effectively.

To equip participants with the knowledge and skills to design, train, and deploy reinforcement learning (RL) algorithms for solving real-world problems across diverse industries.

Day 1: Foundations of Reinforcement Learning

• Overview of Reinforcement Learning (RL)
• RL Application
• Introduction to Sequential Decision
• Markov Decision Process (MDP)
• RL Algorithm Components
• Types of RL Algorithm
• Exploration & Exploitation

Day 2: Reinforcement Learning Algorithms

• A Taxonomy of RL Algorithm
• Q-Learning Algorithm
• Examples for Q-Learning Algorithm
• Advantage & Limitation of Q-Learning
• Deep Q-Network (DQN) Algorithm
• Examples for DQN Algorithm
• Deep NN Process
• Exploration & Exploitation Balancing

Day 3: RL Application and simulation

• RL Simulation using Open AI gym
• RL different simulation Environments & Python
• RL simulation using NS3-Gym
• RL Application