On Reward Function for Survival
  Obtaining a survival strategy (policy) is one of the fundamental problems of
biological agents. In this paper, we generalize the formulation of previous
research related to the survival of an agent and we formulate the survival
problem as a maximization of the multi-step survival probability in future time
steps. We introduce a method for converting the maximization of multi-step
survival probability into a classical reinforcement learning problem. Using
this conversion, the reward function (negative temporal cost function) is
expressed as the log of the temporal survival probability. And we show that the
objective function of the reinforcement learning in this sense is proportional
to the variational lower bound of the original problem. Finally, We empirically
demonstrate that the agent learns survival behavior by using the reward
function introduced in this paper.
