Towards Training an Agent in Augmented Reality World with Reinforcement Learning

Reinforcement learning (RL) helps an agent to learn an optimal path within a specific environment while maximizing its performance. Reinforcement learning (RL) plays a crucial role on training an agent to accomplish a specific job in an environment. To train an agent an optimal policy, the robot must go through intensive training which is not cost-effective in the real-world. A cost-effective solution is required for training an agent by using a virtual environment so that the agent learns an optimal policy, which can be used in virtual as well as real environment for reaching the goal state. In this paper, a new method is purposed to train a physical robot to evade mix of physical and virtual obstacles to reach a desired goal state using optimal policy obtained by training the robot in an augmented reality (AR) world with one of the active reinforcement learning (RL) techniques, known as Q-learning.