nonmodelbased
Nonmodelbased refers to a class of algorithms and approaches, particularly in the field of reinforcement learning, that do not rely on constructing an explicit model of the environment's dynamics. This means that instead of learning a representation of how the environment transitions between states and what rewards are received, these algorithms learn directly from experience.
In contrast to modelbased methods, which first try to learn the transition probabilities and reward functions
The primary advantage of nonmodelbased approaches is their simplicity and directness. They can often be easier
Nonmodelbased algorithms are widely used in robotics, game playing, and control systems where online learning from