Q-Learning: Mastering Value-Based Reinforcement Learning
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Course Modules:
Module 1: Introduction to Q-Learning
What is Q-Learning?
Differences from other RL approaches (model-based, policy-based)
Applications and use cases
Module 2: The Q-Function and Bellman Equation
Understanding the Q-value: Q(s, a)
Bellman equation for Q-value updates
Learning rate, discount factor, and how they affect learning
Module 3: The Q-Learning Algorithm Step-by-Step
Initializing the Q-table
Action selection using epsilon-greedy strategy
Updating values based on reward and future estimates
Module 4: Implementing Q-Learning in Python
Setting up a simple environment (e.g., FrozenLake in OpenAI Gym)
Writing the Q-Learning loop
Visualizing training progress and Q-table updates
Module 5: Challenges and Improvements
Overfitting to known states
Tuning hyperparameters (α, γ, ε)
From Q-tables to Deep Q-Learning (introduction)
Module 6: Capstone Project – Train a Q-Learning Agent
Choose a discrete environment (e.g., Taxi-v3, FrozenLake)
Implement a working Q-Learning agent
Submit the final Q-table, training graphs, and a brief report on policy performance
Tools & Technologies Used:
Python
OpenAI Gym
NumPy, Matplotlib
Optional: Seaborn for heatmap visualization
Target Audience:
Beginner to intermediate AI learners
Developers and students learning reinforcement learning
Engineers exploring agent-environment systems
Researchers or hobbyists building simple RL applications
Global Learning Benefits:
Gain a strong understanding of how AI learns optimal strategies
Apply Q-Learning to gamified, grid-world, or logistics problems
Transition confidently into Deep Q-Networks (DQN) and advanced RL topics
Build, test, and share your own RL agent with real results
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