Abstract:
A multi agent system(MAS) in the context of artificial intelligence refers to a system
composed of multiple interacting agents, which can be entities or robots. These agents work
together or compete to achieve individual or collective goals. Consensus is a decision making
process that aims to achieve the agreement of the majority of entities involved, it involves
collaborative efforts to reach a decision that is mutually acceptable to all involved. Rein-
forcement Learning is an AI algorithm utilized with robots and autonomous vehicles to learn
optimal behaviors by interacting with simulated environments, thereby facilitating safer and
more efficient real world decision making. In this work, the focus is on the application of
RL technique Q-learning using E-puck robots. The E-puck employs Q-learning to deter-
mine the most suitable actions to take within its environment. The concept of consensus
is examined within the context of multi agent systems, where E-puck robots constitute the
agents. Consensus, in this scenario, pertains to the agreement reached among the E-puck
agents regarding their relative positions or distances. Establishing consensus among MAS
and ensuring efficient communication and coordination among E-puck robots pose significant
challenges, yet achieving it holds immense potential for enhancing MAS and optimizing au-
tomation and decision making processes. Using RL algorithm along with webots simulation
and E-puck robots the frameworks can help different agents to navigate toward a common
destination while avoiding obstacles and maintaining consensus. This approach will be ben-
eficial for various sectors, including healthcare assistance, defense, warehouse management,
and even agricultural automation.
