What are the different reinforcement learning techniques?

What are the different reinforcement learning techniques?

Reinforcement learning techniques include Q-learning, Deep Q-Networks (DQN), Policy Gradients, and Actor-Critic methods. Each has its strengths and weaknesses.

Can you explain Q-learning in more detail?

Q-learning is a model-free reinforcement learning algorithm that learns the value of an action in a particular state. It uses a Q-table to store values and updates them based on the rewards received.

How does Deep Q-Networks differ from traditional Q-learning?

Deep Q-Networks use neural networks to approximate the Q-values, allowing them to handle larger state spaces and more complex environments.

What are the advantages of using Policy Gradients?

Policy Gradients directly optimize the policy, which can lead to better performance in high-dimensional action spaces and continuous action environments.