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

A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum