Implementation of Q Learning and Deep Q Network For Controlling a Self Balancing Robot Model
This work addresses a domain-specific control problem for robotics, but it is incremental as it applies existing methods to a new model without major innovations.
The paper tackled the problem of controlling a self-balancing robot model by implementing Q Learning and Deep Q Network (DQN) reinforcement learning methods, with results showing the robot learning to stay balanced and accumulate rewards based on time within specified limits, though no concrete numbers are provided.
In this paper, the implementation of two Reinforcement learnings namely, Q Learning and Deep Q Network(DQN) on a Self Balancing Robot Gazebo model has been discussed. The goal of the experiments is to make the robot model learn the best actions for staying balanced in an environment. The more time it can stay within a specified limit , the more reward it accumulates and hence more balanced it is. Different experiments with different learning parameters on Q Learning and DQN are conducted and the plots of the experiments are shown.