Inverted Pendulum as a Classic Example

Among various nonlinear systems, the inverted pendulum represents a classic example. To achieve control of an inverted pendulum, reinforcement learning serves as a highly effective approach. Reinforcement learning is a machine learning method that improves its behavior through trial-and-error interactions to achieve optimal outcomes. In addressing the inverted pendulum control problem, we can employ reinforcement learning algorithms to design a controller that maintains the pendulum's balanced position. This controller continuously enhances its performance through iterative learning, thereby achieving superior control results. Key implementation aspects typically involve defining state spaces (e.g., angle and angular velocity), action spaces (motor torque commands), and reward functions (penalizing deviations from balance). Common algorithms like Q-learning or Deep Q-Networks (DQN) can be applied where the agent learns optimal policies through value iteration or neural network approximations. Thus, reinforcement learning proves to be a powerful methodology for solving control problems in nonlinear systems such as the inverted pendulum.