Mobile robots are being popular due to their extensive application in different hazardous/ unapproachable areas, such as outer space, underwater explorations, underground coal mines monitoring, inspection in chemical/toxic/ nuclear factories etc. The conventional/ classical robotics may not serve the purpose well if these environments are totally unknown/unpredictable (even the programmer cannot imagine it). In such cases robot learning may be the best option. Learning from the past experiences, is one such way for real time application of robots for completely unknown environments. Reinforcement learning is one of the best learning methods for robots using a constant system-environment interaction. Both single and multi-agent concepts are available for implementation of learning. The current research work describes a multi-agent based reinforcement learning using the concept of behaviour-based robotics for autonomous exploration of mobile robots.