Applying continuous action reinforcement learning automata(CARLA) to global training of hidden Markov models

In this research, we have employed global search and global optimization techniques based on simulated annealing (SA) and continuous action reinforcement learning automata (CARLA) for global training of hidden Markov models. The main goal is comparing CARLA method to other continuous global optimization methods like SA. Experimental results show that the CARLA outperforms SA. This is due to the fact that CARLA is a continuous global optimization method with memory and SA is a memoryless one.