Mobile Agent Control in Intelligent Space using Reinforcement Learning

Finding the safest shortest path in an unknown environment is a fundamental task in mobile robotics. To emulate the human adaptibility in this field, we can use the Intelligent Space concept. The Intelligent Space is a distributed sensory system, which is the background infrastructure to observe human walking in a limited area. The observation of human beings is applied to create a walkable area map of the environment and this map is applied to a learning framework to find the safest path through the environment. The proposed learning framework applies Temporal Difference learning. The main contribution of this paper is that it integrates the Reinforcement Learning and the Intelligent Space concept.