Path Planning of Mobile Robot Using Reinforcement Learning

In complex planning and control operations tasks like manipulating objects, assisting experts in various fields, navigating outdoor environments, exploring uncharted territory, modern robots are designed to complement or completely replace humans. Even for those skilled robot programming, designing a schema such carry out these is typically challenging process that necessitates starting from scratch with new distinct controller each task. The designer must consider the wide range of circumstances might encounter. This kind manual programming expensive time consuming. It would be more beneficial if could learn task on its own rather than having preprogrammed perform all tasks. this paper, method path known environment implemented using Q-Learning by finding an optimal specified ending point.