Mis on mudelipõhine RL ja millised on selle eelised mudelivaba RL ees?

Model-based Reinforcement Learning (RL) is an approach where the agent constructs or uses a model of the environment (transition model) to predict the consequences of its actions. This allows for planning and decision-making using simulations within the model.

Unlike model-free RL, where the agent learns directly from interaction experience with the environment without an explicit model, model-based RL uses the model for more efficient learning.

Advantages of model-based RL:

• Higher sample efficiency — the agent can learn faster by using the model to generate additional data and plan.
• Planning capability — future states can be predicted, and optimal actions can be chosen in advance.
• Flexibility — when the environment changes, only the model needs to be updated, not the entire strategy.

However, implementing model-based RL is more challenging because it requires building an accurate model of the environment, which is not always possible.

Example: in a game, the agent can use a physics model to simulate moves and select the best strategy, instead of trying all actions directly in the real game.