A Huber reward function-driven deep reinforcement learning solution for cart-pole balancing problem

Lots of learning tasks require experience learning based on activities performed in real scenarios which are affected by environmental factors. Therefore, real-time systems demand a model to learn from working experience-such as physical object properties-driven system models, trajectory prediction, and Atari games. This experience-driven learning model uses reinforcement learning which is considered as an important research topic and needs problem-specific reasoning model simulation. In this research paper, cart-pole balancing problem is selected as a problem where the system learns using Q-learning and Deep Q network reinforcement learning approaches. Pragmatic foundation of cart-pole problem and its solution with the help of Q learning and DQN reinforcement learning model are validated, and a comparison of achieved outcome in the form of accuracy and fast convergence is presented. An unexperienced Huber loss function is applied on cart-pole balancing problem, and results are in favor of Huber loss function in comparison with mean-squared error loss function. Hence, experimental study suggests the use of DQN with Huber loss reward function for fast learning and convergence of cart pole in balanced condition.