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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

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

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

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

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: Conceptualizing an A2C Implementation

Intermediate Reinforcement Learning Techniques

Prerequisites: Familiarity with fundamental Reinforcement Learning concepts (Markov Decision Processes, value functions, basic Q-learning, SARSA) and Python programming with a deep learning library (TensorFlow or PyTorch) is required.

Level: Intermediate

What You'll Learn

Function Approximation
Understand why and how to use function approximators (like neural networks) in RL.
Deep Q-Networks (DQN)
Implement and understand the components of DQN, including experience replay and target networks.
DQN Variants
Learn improvements to DQN such as Double DQN and Dueling DQN.
Policy Gradient Methods
Grasp the theory behind policy gradients and implement the REINFORCE algorithm.
Actor-Critic Methods
Understand the architecture and advantages of Actor-Critic algorithms like A2C/A3C.
Algorithm Implementation
Gain practical experience implementing these intermediate RL algorithms.

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