We apply a deep learning model to the QWOP flash game, which requires control of a
ragdoll athlete using only the keys “Q”, “W”, “O”, and “P”. The model is a convolutional neural
network trained with Q-learning. By training the model with only raw pixel input, we show that
our model is capable of successfully learning a control policy associated with playing QWOP.
This model was successfully applied to a non-deterministic control environment in the form of a
ragdoll physics flash game.