Programming robots is a useful tedious task, so there is growing interest in building robots which can learn by themselves. This paper describes the Reinforcement Learning and teaching approach like Queue Learning (Q-Learning) to be implemented for robotics technology environment navigation and exploration. Q – Learning algorithm is one of the widely used online learning methods in robotics; it is simple, efficient, and not need to complex process as in adaptive system. The aim of this work is to empower the agent to learn a certain goal directed navigation strategy and to generate a shortest path in static environment which contain static obstacles; it uses one of the important intelligent search methods the “heuristic”. It makes a necessary modification for the search algorithm to suit the way of solving the problem. In our approach of learning from demonstration, the robot learns a reward function from the demonstration and a task model from repeated attempts (trials) to perform the task. A simplified reinforcement learning algorithm based on one-step Q-Learning that is optimized in speed and memory consumption is proposed and implemented in Visual Basic language (VB). The robot can be built using stepper motors and

Abstract

The effect of steel fiber on the deflection of self- compacted slabs under fire (6000c) was investigated in this study. Three specimens were tested experimentally and numerically ( by using sophisticated finite element programme ANSYS 7.0) to determine the deflection of these specimens under two point load after burned under (6000c) in a tested furnace for four hours. Numerical study by using ANSYS programme is performed to calculate the critical temperature and the temperature through the slabs with steel fiber content of (0%,0.2%, and 0.5%). Another six model slabs with steel fiber content of (0.5%) were studied numerically by using ANSYS 7.0 to investigate the effect of arrange of parameters on the fire performance of self- compacted steel fiber reinforced concrete slabs. The main factors that influence the fire resistance of self- compacted steel fiber reinforced concrete slabs are: slab thickness, concrete cover thickness , moisture content. The experimental results showed that the deflection of burned slab with steel fiber of (0.2%) decreased to 30% than the deflection of burned slab without steel fiber under the same failure load. While the deflection of burned slab with steel fiber of (0.5%) decreased to 50% than the deflection of burned slab without steel fiber under the same failure load. On the other hand , the deflection results were checked with finite elements method by using sophisticated finite element programme (ANSYS 7.0) and it was found that the results were acceptable and the difference was not more than 9%. The results from thermal analysis showed that the temperature decrease with the increase in the concrete depth of the self compacted steel fiber reinforced concrete slabs, while the critical temperature for the slabs with steel fiber of (0%,0.2%,0.5%) were (2500c, 3500c, 5800c) respectively. Parametric study results showed that the slab thickness dose not have significant effect on the fire resistance of the self compacted steel fiber reinforced concrete slabs, while concrete cover thickness has a significant effect on the fire resistance of the self compacted steel fiber reinforced concrete slabs. Fire resistance increases with an increase in the moisture content of the concrete in the slabs.