Learning Agents Using Q-Learning: Theory and Code - Agentic AI Course Lecture 13

Overview

Dive into Q-Learning for reinforcement learning with both theoretical foundations and Python implementation. Master learning agents, Q-tables, epsilon-greedy strategies, and code a simple environment from scratch.

Syllabus

• Introduction to Reinforcement Learning (RL)
Overview of RL concepts
Key components: agent, environment, actions, states, rewards
• Foundations of Q-Learning
Definition and purpose of Q-Learning in RL
The Bellman Equation and Q-value updates
Exploration vs. Exploitation dilemma
• The Q-Table
Structure and purpose of the Q-table
Initialization and representation of the Q-table
Updates to the Q-table based on actions and rewards
• Epsilon-Greedy Strategy
Explanation of the epsilon-greedy strategy for exploration
Balancing exploration and exploitation
Modulating epsilon value for learning efficiency
• Implementation in Python
Setting up the Python environment
Coding the Q-table and update mechanism
Implementing epsilon-greedy action selection
• Building a Simple Environment
Designing a basic environment for a learning agent
Defining state space and action space
Reward structure and state transitions
• Coding a Q-Learning Agent
Integrating all components into a functional agent
Running simulations and observing learning progression
• Practical Applications and Enhancements
Scaling to more complex environments
Introducing variations such as Q-Learning with function approximation
• Conclusion and Further Reading
Recap of key concepts
Suggested readings and resources for advanced learning

Subjects

Computer Science