  Although recent model-free reinforcement learning algorithms have been shown
to be capable of mastering complicated decision-making tasks, the sample
complexity of these methods has remained a hurdle to utilizing them in many
real-world applications. In this regard, model-based reinforcement learning
proposes some remedies. Yet, inherently, model-based methods are more
computationally expensive and susceptible to sub-optimality. One reason is that
model-generated data are always less accurate than real data, and this often
leads to inaccurate transition and reward function models. With the aim to
mitigate this problem, this work presents the notion of survival by discussing
cases in which the agent's goal is to survive and its analogy to maximizing the
expected rewards. To that end, a substitute model for the reward function
approximator is introduced that learns to avoid terminal states rather than to
maximize accumulated rewards from safe states. Focusing on terminal states, as
a small fraction of state-space, reduces the training effort drastically. Next,
a model-based reinforcement learning method is proposed (Survive) to train an
agent to avoid dangerous states through a safety map model built upon temporal
credit assignment in the vicinity of terminal states. Finally, the performance
of the presented algorithm is investigated, along with a comparison between the
proposed and current methods.
