Q-learning and robotics

Because it allows the synthesis of behaviors despite the absence of a robot-world interaction model, Q-learning has become the most used learning algorithm for autonomous robotics in applications such as obstacle avoidance, wall following, go-to-the-nest, etc. This is mostly due to neural-based implementations such as multilayer perceptrons trained with backpropagation, or self-organizing maps. Such implementations provide an efficient generalization, i.e., fast learning, and designate the critic - the reinforcement function definition - as the real issue.