Mis on mudelipõhine RL robotites?

Model-based Reinforcement Learning (RL) in robotics is an approach where the agent (robot) constructs or uses a model of the environment for planning and decision-making. Unlike model-free methods, where the agent learns directly from experience, model-based RL involves a stage of learning or utilizing the environment's dynamic model.

Main features:

• Environment model: the agent tries to predict how its actions will affect the state of the environment.
• Planning: using the model, the agent can simulate various scenarios and choose optimal actions.
• More efficient learning: thanks to the model, the agent can learn faster by using simulations instead of real interactions.

In robotics, this is especially important because real experiments can be costly, slow, or dangerous. Model-based RL allows reducing the number of real trials and errors, improving safety and the cost-effectiveness of learning.