What are the differences between various reinforcement learning methods?

What are the main differences between Q-learning and SARSA?

Q-learning is off-policy, meaning it learns the value of the optimal policy regardless of the agent's actions, while SARSA is on-policy, learning the value of the policy being followed.

Can you explain the advantages of using Deep Q-Networks (DQN) over traditional Q-learning?

DQN uses deep neural networks to approximate the Q-values, allowing it to handle high-dimensional state spaces and learn more complex policies.

How does Policy Gradient differ from value-based methods?

Policy Gradient methods directly optimize the policy by adjusting the parameters based on the gradient of expected rewards, while value-based methods focus on estimating the value function.

What are some common applications of these reinforcement learning methods?

Common applications include game playing, robotics, recommendation systems, and autonomous driving.