What are the key differences between reinforcement learning algorithms?

What are the key differences between reinforcement learning algorithms?

Reinforcement learning algorithms vary in their approach to learning from interactions. For example, Q-learning is model-free and focuses on learning the value of actions, while Deep Q-Networks (DQN) use neural networks to approximate these values.

Can you explain how policy gradients differ from value-based methods?

Sure! Policy gradients directly optimize the policy by adjusting the action probabilities, while value-based methods like Q-learning estimate the value of actions and derive the policy from these values.

Which algorithm is better for continuous action spaces?

For continuous action spaces, algorithms like Proximal Policy Optimization (PPO) or Deep Deterministic Policy Gradient (DDPG) are often preferred, as they can handle the complexity of continuous actions more effectively.

What are some common applications of these algorithms?

Common applications include robotics, game playing, and autonomous vehicles, where agents learn to make decisions based on their environment.