Intermediate Reinforcement Learning Techniques
Chapter 1: Revisiting Reinforcement Learning Fundamentals
The Reinforcement Learning Problem Setup
Markov Decision Processes (MDPs) Recap
Value Functions and Bellman Equations
Tabular Solution Methods: Q-Learning and SARSA
Limitations of Tabular Methods
Quiz for Chapter 1
Chapter 2: Deep Q-Networks (DQN)
Introduction to Function Approximation
Using Neural Networks for Q-Value Approximation
The DQN Algorithm Architecture
Experience Replay Mechanism
Fixed Q-Targets (Target Networks)
Loss Function for DQN Training
Hands-on Practical: Implementing DQN for CartPole
Quiz for Chapter 2
Chapter 3: Improvements and Variants of DQN
The Overestimation Problem in Q-Learning
Double DQN (DDQN)
Dueling Network Architectures
Combining DQN Improvements
Prioritized Experience Replay (Brief Overview)
Practice: Implementing Double DQN
Quiz for Chapter 3
Chapter 4: Policy Gradient Methods
Limitations of Value-Based Methods
Direct Policy Parameterization
The Policy Gradient Theorem
REINFORCE Algorithm
Understanding Variance in Policy Gradients
Baselines for Variance Reduction
Hands-on Practical: Implementing REINFORCE
Quiz for Chapter 4
Chapter 5: Actor-Critic Methods
Combining Policy and Value Estimation
Actor-Critic Architecture Overview
Advantage Actor-Critic (A2C)
Asynchronous Advantage Actor-Critic (A3C)
Implementation Considerations for Actor-Critic
Comparison: REINFORCE vs A2C/A3C
Practice: Developing an A2C Implementation
Quiz for Chapter 5
Combining Policy and Value Estimation
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Oct 22, 2025
Fixed the time index. The update was corrected because the error is calculated for the value estimate at the current time step, t, and the notation should align with that time step.
- Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - A comprehensive and authoritative textbook covering the foundations of reinforcement learning, including detailed explanations of policy gradient, value-based methods, and actor-critic architectures.
- Policy Gradients and Actor-Critic, Sergey Levine, 2021 - Lecture notes from a leading university course on deep reinforcement learning, offering a clear and concise academic explanation of policy gradient methods and the fundamental principles of actor-critic algorithms.
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