Softmax selection is one of the most popular
methods for action selection in reinforcement learning. Although
various recently proposed methods may be more effective with
full parameter tuning, implementing a complicated method
that requires the tuning of many parameters can be difficult.
Thus, softmax selection is still worth revisiting, considering the
cost savings of its implementation and tuning. In fact, this
method works adequately in practice with only one parameter
appropriately set for the environment. The aim of this paper
is to improve the variable setting of this method to extend
the bandwidth of good parameters, thereby reducing the cost
of implementation and parameter tuning. To achieve this, we
take advantage of the asymptotic equipartition property in a
Markov decision process to extend the peak bandwidth of softmax
selection. Using a variety of episodic tasks, we show that our
setting is effective in extending the bandwidth and that it yields a
better policy in terms of stability. The bandwidth is quantitatively
assessed in a series of statistical tests.