Reinforcement learning is key research in automatic control, and hierarchical reinforcement learning is a good solution to the problem of the curse of dimensionality. Hierarchical reinforcement learning can only deal with discrete space, but the state and action spaces in robotic automatic control are continuous. In order to deal with continuous spaces in hierarchical reinforcement learning, we...

In this paper, an efficient optimization method based on reinforcement learning automata (RLA) for optimum parameters setting of conventional proportional-integral-derivative (PID) controller for AVR system of power synchronous generator is proposed. The proposed method is Continuous Action Reinforcement Learning Automata (CARLA) which is able to explore and learn to improve control performance...

A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. The aim is to design an automaton, using a reinforcement scheme based on the computational model of wasp behaviour that can determine the best action guided by past ac...

A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. This response may be favourable or unfavourable. The aim is to design an automaton that can determine the best action guided by past actions and responses. Using Stoch...

1. ABSTRACT In this paper we present a reinforcement learning technique based on Learning Automata (LA), more specific Continuous Action Reinforcement Learning Automaton (CARLA), introduced by Howell et. al. in [2]. LA are policy iterators, which have shown good convergence results in discrete action games with independent learners. The approach presented in this paper allows LA to deal with co...

Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, industrial plants and language models are only some of the many real-world tasks involving large numbers of discrete actions for which current methods can be difficult or even impossible to apply. An ability to general...

In this report, we analyze the collective behavior of learning automata which are used in a programming language under development that combines reinforcement learning and symbolic programming [2, 6]. Learning automata can automatically improve their behavior by using a response from a random stationary environment, but when connected with each other, their behavior becomes much complex and har...

Learning Automata (LA) are adaptive decision making devices suited for operation in unknown environments [12]. Originally they were developed in the area of mathematical psychology and used for modeling observed behavior. In its current form, LA are closely related to Reinforcement Learning (RL) approaches and most popular in the area of engineering. LA combine fast and accurate convergence wit...