  The use of mobile robots is being popular over the world mainly for
autonomous explorations in hazardous/ toxic or unknown environments. This
exploration will be more effective and efficient if the explorations in unknown
environment can be aided with the learning from past experiences. Currently
reinforcement learning is getting more acceptances for implementing learning in
robots from the system-environment interactions. This learning can be
implemented using the concept of both single-agent and multiagent. This paper
describes such a multiagent approach for implementing a type of reinforcement
learning using a priority based behaviour-based architecture. This proposed
methodology has been successfully tested in both indoor and outdoor
environments.
