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
Actor-Critic Architecture Overview
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- Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - Comprehensive textbook covering fundamental reinforcement learning concepts, including value functions, policy gradients, and Actor-Critic methods.
- Policy Gradient Methods for Reinforcement Learning with Function Approximation, Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour, 2000 Advances in Neural Information Processing Systems, Vol. 13 - Introduces the policy gradient theorem, which provides the theoretical foundation for updating the Actor's policy parameters in Actor-Critic algorithms.
- Asynchronous Methods for Deep Reinforcement Learning, Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu, 2016 International Conference on Machine Learning (ICML) DOI: 10.48550/arXiv.1602.01783 - Presents A3C, a significant deep Actor-Critic algorithm that effectively implements the architecture described, demonstrating its efficiency in various environments.