  Modeling personality is a challenging problem with applications spanning
computer games, virtual assistants, online shopping and education. Many
techniques have been tried, ranging from neural networks to computational
cognitive architectures. However, most approaches rely on examples with
hand-crafted features and scenarios. Here, we approach learning a personality
by training agents using a Deep Q-Network (DQN) model on rewards based on
psychoanalysis, against hand-coded AI in the game of Pong. As a result, we
obtain 4 agents, each with its own personality. Then, we define happiness of an
agent, which can be seen as a measure of alignment with agent's objective
function, and study it when agents play both against hand-coded AI, and against
each other. We find that the agents that achieve higher happiness during
testing against hand-coded AI, have lower happiness when competing against each
other. This suggests that higher happiness in testing is a sign of overfitting
in learning to interact with hand-coded AI, and leads to worse performance
against agents with different personalities.
