Reinforcement Learning: Deep Q-Networks
In reinforcement learning (RL), Q-learning is a foundational algorithm that helps an agent navigate its environment by learning a policy to maximize cumulative rewards. It does this by updating an action-value function, which estimates the expected utility of taking a specific action in a given state, based on received rewards and future estimations (this doesn't sound familiar? Don't worry as we will go over it later together).
However, traditional Q-learning has its challenges. It struggles with scalability as the state space grows and is less effective in environments with continuous state and action spaces. This is where Deep Q Networks (DQNs) come in. DQNs use neural networks to approximate the Q-values, enabling agents to handle larger and more complex environments effectively.
In this article, we'll dive into Deep Q Networks. We'll explore how DQNs overcome the limitations of traditional Q-learning and discuss the key components that make up a DQN. We'll also walk through implementing a DQN from scratch and applying it to a more complex environment. By the end of this article, you'll have a solid understanding of how DQNs work and how to use them to solve challenging RL problems.
Index
1: Traditional Q-Learning ∘ 1.1: States and Actions ∘ 1.2: Q-Values ∘ 1.3: The Q-Table ∘ 1.4: Learning Process
2: From Q-Learning to Deep Q-Networks ∘ 2.1: Limitations of Traditional Q-Learning ∘ 2.2: Neural Networks
3: The Anatomy of a Deep Q-Network ∘ 3.1: Components of a DQN ∘ 3.2: The DQN Algorithm
4: Implementing a Deep Q-Network from Scratch ∘ 4.1: Setting up the Environment ∘ 4.2: Building the Deep Neural Network ∘ 4.3: Implementing Experience Replay ∘ 4.4: Implementing the Target Network ∘ 4.5: Training the Deep Q-Network ∘ 4.6: Tuning the Model ∘ 4.7: Running the model
1: Traditional Q-Learning
Q-learning guides an agent to learn the best actions to maximize cumulative rewards in an environment. Before diving into Deep Q-Networks, it's good first to review the mechanisms behind its predecessor, Q-learning briefly.
1.1: States and Actions
Imagine you're a robot navigating a maze. Every position you occupy in the maze is called a "state." Each possible move you can make, like moving left, right, up, or down, is an "action." The goal is to figure out which action to take in each state to find the best path through the maze eventually.
1.2: Q-Values
The heart of Q-Learning is the Q-value, denoted as
