Applying C³LRTA*, a color code coordinated LRTA* algorithm on mazes and labyrinths

Multi Agent is regarded as a promising paradigm for future distributed computing. Multi Agent Learning Real Time A* (MALRTA*) is a multi-agent version of Learning Real Time A* (LRTA*) algorithm where multiple agents concurrently and autonomously search to find a solution. In this paper, we propose C³LRTA* algorithm which uses color code for coordination among multiple problem solvers. Each agent observes the color code of the state and select the next move on the basis of this color, in contrast it simply moves randomly in the original MALRTA*. We have applied C³LRTA* to solve randomly generated mazes and labyrinth. Observed results suggests that by using the proposed coordination scheme, we get an improvement in LRTA*. We have evaluated this coordination scheme on a large number of labyrinth and mazes with random obstacles and varying obstacle ratio. Through simulation experiments, we have shown that C³LRTA* is effective in both search time and solution quality in both mazes and labyrinths. In addition, the strategy used in C³LRTA* can be made more efficient if the number of agents and/or obstacle ratio is increased.