This paper outlines the design and implementation of a control algorithm for a selfbalancing robot using Deep Reinforcement Learning, specifically the Deep Q-learning algorithm. Self-balancing robots represent a significant search area in robotics, as they require advanced control strategies to maintain stability and adapt to changing environmental conditions. The primary objective of this research is to develop an intelligent system capable of maintaining balance and performing precise maneuvers in response to disturbances and varying circumstances. The Deep Q-learning algorithm enables a robot to learn optimal control policies by interacting with a simulated environment. In this scenario, the robot receives feedback in the form of rewards based on its actions. By employing a neural network to estimate the Q-value function, the robot learns to link specific environmental states with actions that maximize cumulative rewards. The training process occurs within a controlled simulation environment, addressing challenges such as balancing exploration and exploitation, managing reward sparsity, and ensuring the convergence of the learning model. Experimental results demonstrate that the proposed control algorithm successfully stabilizes the robot, allowing it to stand upright, move forward, and navigate uneven terrain, the Deep Q-Learning-based approach has shown to be robust, efficient, and adaptive, outperforming traditional control methods in terms of dynamic response and flexibility. This work contributes to the advancement of machine learning techniques in robotics, emphasizing the potential of Deep Reinforcement Learning algorithms to address complex control problems. The paper concludes by discussing the strengths and limitations of the developed systems and potential future directions, such as hardware implementation, multi-agent collaboration, and scalability to more complex robotic systems.
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This paper outlines the design and implementation of a control algorithm for a selfbalancing robot using Deep Reinforcement Learning, specifically the Deep Q-learning algorithm. Self-balancing robots represent a significant search area in robotics, as they require advanced control strategies to maintain stability and adapt to changing environmental conditions. The primary objective of this research is to develop an intelligent system capable of maintaining balance and performing precise maneuvers in response to disturbances and varying circumstances. The Deep Q-learning algorithm enables a robot to learn optimal control policies by interacting with a simulated environment. In this scenario, the robot receives feedback in the form of rewards based on its actions. By employing a neural network to estimate the Q-value function, the robot learns to link specific environmental states with actions that maximize cumulative rewards. The training process occurs within a controlled simulation environment, addressing challenges such as balancing exploration and exploitation, managing reward sparsity, and ensuring the convergence of the learning model. Experimental results demonstrate that the proposed control algorithm successfully stabilizes the robot, allowing it to stand upright, move forward, and navigate uneven terrain, the Deep Q-Learning-based approach has shown to be robust, efficient, and adaptive, outperforming traditional control methods in terms of dynamic response and flexibility. This work contributes to the advancement of machine learning techniques in robotics, emphasizing the potential of Deep Reinforcement Learning algorithms to address complex control problems. The paper concludes by discussing the strengths and limitations of the developed systems and potential future directions, such as hardware implementation, multi-agent collaboration, and scalability to more complex robotic systems.
