  This work describes a technique for active rejection of multiple independent
and time-correlated stochastic disturbances for a nonlinear flexible inverted
pendulum with cart system with uncertain model parameters. The control law is
determined through deep reinforcement learning, specifically with a continuous
actor-critic variant of deep Q-learning known as Deep Deterministic Policy
Gradient, while the disturbance magnitudes evolve via independent stochastic
processes. Simulation results are then compared with those from a classical
control system.
