  Using the policy gradient algorithm, we train a single-hidden-layer neural
network to balance a physically accurate simulation of a single inverted
pendulum. The trained weights and biases can then be transferred to a physical
agent, where they are robust enough to to balance a real inverted pendulum.
This hybrid approach of training a simulation allows thousands of trial runs to
be completed orders of magnitude faster than would be possible in the real
world, resulting in greatly reduced training time and more iterations,
producing a more robust model. When compared with existing reinforcement
learning methods, the resulting control is smoother, learned faster, and able
to withstand forced disturbances.
