An Exploration of Deep Learning Methods in Hungry Geese
  Hungry Geese is a n-player variation of the popular game snake. This paper
looks at state of the art Deep Reinforcement Learning Value Methods. The goal
of the paper is to aggregate research of value based methods and apply it as an
exercise to other environments. A vanilla Deep Q Network, a Double Q-network
and a Dueling Q-Network were all examined and tested with the Hungry Geese
environment. The best performing model was the vanilla Deep Q Network due to
its simple state representation and smaller network structure. Converging
towards an optimal policy was found to be difficult due to random geese
initialization and food generation. Therefore we show that Deep Q Networks may
not be the appropriate model for such a stochastic environment and lastly we
present improvements that can be made along with more suitable models for the
environment.
