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We present the first reinforcement-learning model to self-improve itsreward-modulated training implemented through a continuously improving"intuition" neural network. An agent was trained how to play the arcade videogame Pong with two reward-based alternatives, one where the paddle was placedrandomly during training, and a second where the paddle was simultaneouslytrained on three additional neural networks such that it could develop a senseof "certainty" as to how probable its own predicted paddle position will be toreturn the ball. If the agent was less than 95% certain to return the ball, thepolicy used an intuition neural network to place the paddle. We trained botharchitectures for an equivalent number of epochs and tested learningperformance by letting the trained programs play against a near-perfectopponent. Through this, we found that the reinforcement learning model thatuses an intuition neural network for placing the paddle during reward trainingquickly overtakes the simple architecture in its ability to outplay thenear-perfect opponent, additionally outscoring that opponent by an increasinglywide margin after additional epochs of training.

Regulating Reward Training by Means of Certainty Prediction in a Neural Network-Implemented Pong Game