Machine learning-based control is an emerging and promising area in
control applications. Reinforcement Learning is an attractive part of machine
learning. In this study, five different Reinforcement Learning algorithms (i.e.
Deep-Q Networks, Trusted Region Policy Optimization, Proximal Policy
Optimization, Asynchronous Advantage Actor-Critic and Actor-Critic using
Kronecker-Factored Trust Region) and their performances at three different
levels of time step (i.e. 105, 106 and 107) are
presented on the inverted pendulum on cart since it is a basic problem studied
widely in control.
Machine learning-based control is an emerging and promising area in
control applications. Reinforcement Learning is an attractive part of machine
learning. In this study, five different Reinforcement Learning algorithms (i.e.
Deep-Q Networks, Trusted Region Policy Optimization, Proximal Policy
Optimization, Asynchronous Advantage Actor-Critic and Actor-Critic using
Kronecker-Factored Trust Region) and their performances at three different
levels of time step (i.e. 105, 106 and 107) are
presented on the inverted pendulum on cart since it is a basic problem studied
widely in control.