Model-free-reinforcement-learning — Model-Free Reinforcement Learning

Fact

AI learns what to do by trying, without first building a world model.

In Plain Words

It is like learning a video game boss fight with no guide. You get squished, respawn, and slowly stop doing dumb stuff.

The AI tries actions and keeps the ones that pay off. You see it in game bots and robot control.

Related Concepts

• Model-Based Reinforcement Learning
• Q-Learning
• Policy Gradient
• Deep Reinforcement Learning

MBRL
Model-free learning skips the world model and learns by trial and error.

Q-Learning
Q-Learning is a classic model-free method for learning action scores.

Policy Gradient
Policy Gradient is another model-free path that learns the policy directly.

Deep RL
Deep RL often uses neural networks to run model-free methods.