Imitation learning of fish and swarm behavior with Recurrent Neural Networks
In the field of collective behavior group-level phenomena emerge from interactions between individuals. To study inter-individual rules the Landgraf lab has built a robotic guppy that replaces live anmials in the shoal, RoboFish. The primary purpose of this thesis is to examine if the pair interaction behavior of female guppies can be learned by recurrecnt neural networks via supervised learning and to develop the software components required to have the RoboFish system run the resulting models in the real world. Two distinct datasets are studied and RNN models trained to try to imitate the behavior seen in them: One dataset was synthetically generated from a simple deterministic model as a baseline and one was captured from live fish. Training different kinds of RNNs on the datasets revealed a capability of a simple stacked RNN to learn swarm behavior, further improved upon by putting a ConvLSTM input network in front of it. I was also successful in showing that a more complex network was able to learn some basic interaction behavior as seen in real fish.