Emergence of Prediction by Reinforcement Learning Using a Recurrent Neural Network
Figure 1
Object catching task and a recurrent neural network. An agent moves up or down and catches a moving object. The initial direction of motion and velocity of the object are chosen randomly for every episode. The invisibility area is also chosen randomly in the range of coordinates of the object and y coordinate of the agent are input to an Elman-type recurrent neural network. Each input signal represents local information, as shown in Figure 2.