  Reinforcement learning is commonly concerned with problems of maximizing
accumulated rewards in Markov decision processes. Oftentimes, a certain goal
state or a subset of the state space attain maximal reward. In such a case, the
environment may be considered solved when the goal is reached. Whereas numerous
techniques, learning or non-learning based, exist for solving environments,
doing so optimally is the biggest challenge. Say, one may choose a reward rate
which penalizes the action effort. Reinforcement learning is currently among
the most actively developed frameworks for solving environments optimally by
virtue of maximizing accumulated reward, in other words, returns. Yet, tuning
agents is a notoriously hard task as reported in a series of works. Our aim
here is to help the agent learn a near-optimal policy efficiently while
ensuring a goal reaching property of some basis policy that merely solves the
environment. We suggest an algorithm, which is fairly flexible, and can be used
to augment practically any agent as long as it comprises of a critic. A formal
proof of a goal reaching property is provided. Simulation experiments on six
problems under five agents, including the benchmarked one, provided an
empirical evidence that the learning can indeed be boosted while ensuring goal
reaching property.
