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 Reinforcement Learning Problem Setup
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- Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - Introduces the foundational concepts of RL, including the agent-environment interface, states, actions, rewards, policies, and returns, defining the problem setup.
- CS234: Reinforcement Learning (Spring 2024 Lecture 1: Introduction, The RL Problem), Emma Brunskill, 2024 Stanford University Course Materials (Stanford Online (via YouTube)) - Offers a comprehensive academic introduction to the RL problem setup, describing agents, environments, states, actions, rewards, policies, and the concept of return within a structured course context.
- Spinning Up in Deep RL: Key Concepts in RL, Joshua Achiam, 2018 (OpenAI) - A resource defining key RL concepts such as the agent-environment interface, states, actions, rewards, and policies, aiding practitioners in understanding the setup.
- Dynamic Programming and Markov Processes, Ronald A. Howard, 1960 (MIT Press) - A seminal publication that introduces Markov Decision Processes, the mathematical framework for the reinforcement learning problem setup, discussing state transitions, actions, and optimal policies.