  The multi-armed bandit(MAB) is a classical sequential decision problem. Most
work requires assumptions about the reward distribution (e.g., bounded), while
practitioners may have difficulty obtaining information about these
distributions to design models for their problems, especially in non-stationary
MAB problems. This paper aims to design a multi-armed bandit algorithm that can
be implemented without using information about the reward distribution while
still achieving substantial regret upper bounds. To this end, we propose a
novel algorithm alternating between greedy rule and forced exploration. Our
method can be applied to Gaussian, Bernoulli and other subgaussian
distributions, and its implementation does not require additional information.
We employ a unified analysis method for different forced exploration strategies
and provide problem-dependent regret upper bounds for stationary and
piecewise-stationary settings. Furthermore, we compare our algorithm with
popular bandit algorithms on different reward distributions.
