Inverted Pendulum Control using Twin Delayed Deep Deterministic Policy Gradient with a Novel Reward Function

In recent years, deep reinforcement learning has attracted significant attention from both industry and academia and is used in solving various complex problems. In this paper, an actor-critic algorithm called twin delayed deep deterministic policy gradient (TD3) is used to solve the Inverted Pendulum control problem. For the same purpose a novel dynamic and improved reward function is presented. The choice of reward function was based on dynamic behaviour of the pendulum. The experiment results show the dominance of combination of TD3 and proposed reward function over other methods that were used to solve this problem in terms of performance specification and reward stabilization.