Exploration of genetic network programming with two-stage reinforcement learning for mobile robot

This paper observes the exploration of Genetic Network Programming Two-Stage Reinforcement Learning for mobile robot navigation. The proposed method aims to observe its exploration when inexperienced environments used in the implementation. In order to deal with this situation, individuals are trained firstly in the training phase, that is, they learn the environment with ϵ-greedy policy and learning rate α parameters. Here, two cases are studied, i.e., case A for low exploration and case B for high exploration. In the implementation, the individuals implemented to get experience and learn a new environment on-line. Then, the performance of learning processes are observed due to the environmental changes

Genetic Network Programming with Reinforcement Learning and its Performance Evaluation

Abstract. A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP ” has been proposed. GNP represents its solutions as graph structures, which can improve the expression ability and performance. Since GA, GP and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we propose GNP with Reinforcement Learning (GNP with RL) in this paper in order to search solutions quickly. Evolutionary algorithm of GNP makes very compact graph structure which contributes to reducing the size of the Q-table and saving memory. Reinforcement Learning of GNP improves search speed for solutions because it can use the information obtained during task execution.

Online Identification And Control of A PV-Supplied DC Motor Using Universal Learning Networks

This paper describes the use of Universal Learning Networks (ULNs) in the speed control of a separately excited DC motor drives fed from Photovoltaic (PV) generators through intermediate power converters. Two ULNs-based identification and control are used. Their free parameters are updated online concurrently by the forward propagation algorithm. The identifier network is used to capture and emulate the nonlinear mappings between the inputs and outputs of the motor system. The controller network is used to control the converter duty ratio so that the motor speed can follow an arbitrarily reference signal. Moreover the overall system can operate at the Maximum Power Point (MPP) of the PV source. The simulation results showed a good performance for the controller and the identifier during the training mode and the continuous running mode as well. 1