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
Experience Replay Mechanism
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- Human-level control through deep reinforcement learning, Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Gimel, Andriy Blun, Daan Wierstra, John Wenzeslav, Remi Munos, 2015 Nature, Vol. 518 DOI: 10.1038/nature14236 - This foundational paper introduces the Deep Q-Network (DQN) algorithm, including the experience replay mechanism, which addresses issues of correlated data and unstable learning in deep reinforcement learning.
- Prioritized Experience Replay, Tom Schaul, John Quan, Ioannis Antonoglou, David Silver, 2016 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1511.05952 - This paper proposes a method to improve experience replay by prioritizing important transitions, making the learning process more efficient and effective than uniform sampling.
- Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - A comprehensive textbook that provides foundational concepts of reinforcement learning, including a thorough explanation of Deep Q-Networks and the role of experience replay. 2nd edition.
- David Silver's Reinforcement Learning Course - Lecture 6: Value Function Approximation, David Silver, 2015 (University College London (UCL)) - This lecture from a renowned course explains the principles of value function approximation, including a clear discussion of Deep Q-Networks (DQN) and the function of experience replay.