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
The Policy Gradient Theorem
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- Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - Definitive textbook covering the theoretical foundations of reinforcement learning, including a detailed derivation and explanation of the Policy Gradient Theorem and its applications.
- Simple statistical gradient-following algorithms for connectionist reinforcement learning, Ronald J. Williams, 1992 Machine Learning, Vol. 8 (Kluwer Academic Publishers, Boston) DOI: 10.1007/BF00992696 - Introduces the REINFORCE algorithm, a direct application of the Policy Gradient Theorem, and provides foundational insights into policy gradient methods.
- CS285: Deep Reinforcement Learning, Lecture 5: Policy Gradients I, Sergey Levine, 2020 (UC Berkeley) - Lecture notes from a deep reinforcement learning course, offering a modern perspective on policy gradient methods, their derivation, and practical considerations.