In the dissertation combined reinforcement learning (RL) and simulated annealing (SA) concepts, problems, proposed solutions, algorithms and application examples are shown. RL models a decision maker as a goal-driven agent aiming to reach goal states in the problem representation state space. The agent takes different choices among the numerous possibilities, but each choice can make different impact in the environment. Each decision has some effect being expressed in the form of numeric honor or dishonor, in a reward value. The agent utilizes the feedback to recognize which actions are honored and which are not. The agent then tries to govern its decision sequence into the direction that maximizes the “environment’s satisfaction”. The concept of SA is based on the analogy of how liquids freeze. There an initially high temperature and disordered melt is slowly cooled down and reaches thermal equilibrium. While in annealing the temperature parameter bounds are straightforward, in SA they might be dependent on the problem and its numeric representation. This dissertation gives a method which can be used for defining temperature bounds in RL environment.

Данное издание не является оригинальным. Книга печатается по технологии принт-он-деманд после получения заказа.