Robot Navigation in Complex and Initially Unknown Environments

Abstract

This paper proposes an agent that uses reinforcement learning methods to guarantee its autonomy to navigate. Differently from the representations commonly found in the technical literature, the reinforcement signal from the environment is represented Through reward and penalty surfaces to endow the agent with The ability to plan and to behave reactively. The agent solves the goal-directed reinforcement learning problem in which a first learning stage finds a path, based only on local information, and this path is a meliorated through further training. The proposed task is executed in an initially unknown environment, then, the initial viable solution is improved, employing a variable learning rate for the reward evaluation. The simulations suggest that the agent always reach the target, even in complex environments.