In reinforcement learning (RL), DQN (Deep Q-Network) and Actor-Critic are two foundational algorithms that address different challenges in learning optimal policies. Here's a breakdown of their key differences and use cases:

🔍 What Are DQN and Actor-Critic?

  • DQN
    A model-free algorithm that uses a deep neural network to approximate the Q-value function. It extends Q-learning by adding a neural network to handle high-dimensional state spaces.

    Deep_Q_Network

  • Actor-Critic
    A policy-gradient method that combines two components:

    • Actor: Selects actions based on the current policy.
    • Critic: Evaluates the action's quality (value function) to guide the actor.
    Actor_Critic

📊 Key Differences

Feature DQN Actor-Critic
Type Q-learning based Policy-gradient based
Memory Uses experience replay Typically doesn't require it
Stability More stable with target network Can be more volatile without proper design
Complexity Simpler architecture More complex with dual networks

🧩 Advantages and Use Cases

  • DQN

    • Pros: Easy to implement, effective for high-dimensional states.
    • Cons: Struggles with continuous action spaces.
    • Use Cases: Games like Atari (e.g., Pong, Breakout).

  • Actor-Critic

    • Pros: Better handling of continuous actions, faster convergence.
    • Cons: Requires careful balancing of actor and critic updates.
    • Use Cases: Robotics, autonomous driving, and complex environments.

🌐 Further Reading

For a deeper dive into DQN, check our tutorial on Understanding Deep Q-Networks.
To explore Actor-Critic in detail, visit Policy Gradient Methods.

📌 Visual Comparison

DQN_vs_Actor_Critic

Both methods are critical for mastering reinforcement learning—choose the one that fits your problem's complexity! 🚀