A Definition of Happiness for Reinforcement Learning Agents
  What is happiness for reinforcement learning agents? We seek a formal
definition satisfying a list of desiderata. Our proposed definition of
happiness is the temporal difference error, i.e. the difference between the
value of the obtained reward and observation and the agent's expectation of
this value. This definition satisfies most of our desiderata and is compatible
with empirical research on humans. We state several implications and discuss
examples.
