  The reinforcement learning algorithms that focus on how to compute the
gradient and choose next actions, are effectively improved the performance of
the agents. However, these algorithms are environment-agnostic. This means that
the algorithms did not use the knowledge that has been captured by trajectory.
This poses that the algorithms should sample many trajectories to train the
model. By considering the essence of environment and how much the agent learn
from each scenario in that environment, the strategy of the learning procedure
can be changed. The strategy retrieves more informative trajectories, so the
agent can learn with fewer trajectory sample. We propose Where2Start algorithm
that selects the initial state so that the agent has more instability in
vicinity of that state. We show that this kind of selection decreases number of
trajectories that should be sampled that the agent reach to acceptable reward.
Our experiments shows that Where2Start can improve sample efficiency up to 8
times. Also Where2Start can combined with most of state-of-the-art algorithms
and improve that robustness and sample efficiency significantly.
