  Inverted pendulums constitute one of the popular systems for benchmarking
control algorithms. Several methods have been proposed for the control of this
system, the majority of which rely on the availability of a mathematical model.
However, deriving a mathematical model using physical parameters or system
identification techniques requires manual effort. Moreover, the designed
controllers may perform poorly if system parameters change. To mitigate these
problems, recently, some studies used Reinforcement Learning (RL) based
approaches for the control of inverted pendulum systems. Unfortunately, these
methods suffer from slow convergence and local minimum problems. Moreover, they
may require hyperparameter tuning which complicates the design process
significantly. To alleviate these problems, the present study proposes an
LQR-based RL method for adaptive balancing control of an inverted pendulum. As
shown by numerical experiments, the algorithm stabilizes the system very fast
without requiring a mathematical model or extensive hyperparameter tuning. In
addition, it can adapt to parametric changes online.
