An Extension of Genetic Network Programming with Reinforcement Learning Using Actor-Critic

Abstract

A new graph-based evolutionary algorithm named learning methods is applied to GNP (GNP with Actor-"Genetic Network Programming, GNP" has been already Critic, GNP-AC). The proposed method is applied to the proposed. GNP represents its solutions as graph structures, controller of the Khepera simulator and its performance is which can improve the expression ability and performance. In evaluated. The evolution of the proposed method determines addition, GNP with Reinforcement Learning (GNP-RL) was evalucture ofoGNP,oi.of the opos bethodndes, and proposed a few years ago. Since GNP-RL can do reinforcement the structure of GNP, i.e., the connections between nodes, and learning during task execution in addition to evolution after Actor-Critic efficiently determines node functions, i.e., the task execution, it can search for solutions efficiently. In this speed of the wheels and the parameters of judgment nodes.paper, GNP with Actor-Critic (GNP-AC) which is a new This paper is organized as follows. In the next section, the type of GNP-RL is proposed. Originally, GNP deals with . . . . ' discrete information, but GNP-AC aims to deal with continuous algorithm of the proposed method is described. Section III information. The proposed method is applied to the controller shows the results of the simulations. Section IV is devoted of the Khepera simulator and its performance is evaluated. to conclusions.

A Survey of Approximate Dynamic Programming

“…GNP represents its solutions as graph structures, which can improve the expression ability and performance. In addition, GNP with Reinforcement Learning (GNP-RL) was proposed a few years ago [29], in [28], GNP with Actor-Critic (GNP-AC) which is a new type of GNP-RL was proposed. Originally, GNP deals with discrete information, but GNP-AC aims to deal with continuous information.…”