Duration 3 days – 21 hrs
Overview
This advanced course is designed to provide deep expertise in Reinforcement Learning (RL)—a powerful paradigm where agents learn through interaction with environments. Participants will learn the theoretical foundations and practical implementations of Q-Learning, Deep Q-Networks (DQN), and explore real-world applications in robotics, gaming, and autonomous systems using Python and deep learning libraries like TensorFlow or PyTorch.
Objectives
- Understand the core principles of reinforcement learning: rewards, policies, value functions, and environments
- Implement Q-Learning and Deep Q-Networks from scratch and using libraries
- Train agents in simulated environments (e.g., OpenAI Gym)
- Apply RL to real-world problems such as robotic control and decision-making in games
- Tune and stabilize training using experience replay and target networks
Audience
- Machine learning engineers and AI researchers exploring autonomous decision systems
- Robotics developers and engineers building adaptive control systems
- Game developers implementing AI agents for competitive or dynamic environments
- Technical professionals with experience in deep learning and ML seeking to expand into RL
Prerequisites
- Strong Python programming skills
- Proficiency with machine learning and deep learning concepts
- Experience with neural networks using TensorFlow or PyTorch
- Familiarity with probability, linear algebra, and calculus fundamentals
Course Content
Day 1: Foundations of Reinforcement Learning
- Introduction to RL: agent, environment, state, action, reward
- Policies, value functions, Bellman equations
- Exploration vs. exploitation
- Q-Learning algorithm explained
- Hands-on: Implement Q-Learning in a grid world simulation
Day 2: Deep Q-Networks (DQN)
- From Q-Learning to Deep Q-Networks
- Neural network-based approximators for value functions
- Experience replay and target network stabilization
- Hands-on: Train a DQN agent in OpenAI Gym (e.g., CartPole, LunarLander)
Day 3: Applications in Robotics and Gaming
- Applying RL to robotic control tasks (navigation, manipulation)
- Game AI: agent training for dynamic environments (Atari, custom games)
- Performance evaluation and reward shaping
- Final project: Build and train a DQN agent for a real-world-inspired task
- Future directions: Policy Gradient methods, Actor-Critic, and beyond
