In reinforcement learning, model-free methods are used when:
- There is no prior model of the environment (i.e., no known state transition or reward function).
- The agent learns only by trial and error, using actual experience gained by interacting with the environment.
Examples:
- Monte Carlo methods
- Temporal Difference (TD) learning
Monte Carlo (MC) Methods
Temporal Difference (TD) Learning
