Reinforcement Learning for Real-World Applications
Mastering Reinforcement Learning to Solve Complex Real-World Challenges.
Skills you will gain:
About Program:
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.
Aim: 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.
Program Objectives:
- To introduce participants to foundational and advanced reinforcement learning techniques.
- To enable participants to design, train, and evaluate RL algorithms.
- To explore diverse applications of RL in industries like robotics, healthcare, and finance.
- To emphasize ethical and practical considerations in RL deployments.
- To prepare participants for research and professional roles in RL and AI-driven systems.
What you will learn?
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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- AI and machine learning professionals
- Students and researchers in computer science, robotics, and AI
- Professionals in industries such as finance, healthcare, and automation
- Enthusiasts interested in applying RL to real-world challenges
Career Supporting Skills
Program Outcomes
- By the end of this workshop, participants will:
- Understand RL Fundamentals – Learn MDP, RL algorithms, and decision-making strategies.
- Implement RL Algorithms – Apply Q-Learning, DQN, and policy-based methods.
- Gain Hands-on Experience – Work with OpenAI Gym, NS3-Gym, and Python simulations.
- Explore Real-World Applications – Use RL in robotics, finance, healthcare, and gaming.
- Optimize RL Models – Balance exploration vs. exploitation for performance improvement.
- Advance Career in AI – Acquire skills for AI, automation, and intelligent systems roles.
Participants will leave equipped to build and implement RL models in real-world scenarios. 🚀