  Partial monitoring is a generic framework of online decision-making problems
with limited observations. To make decisions from such limited observations, it
is necessary to find an appropriate distribution for exploration. Recently, a
powerful approach for this purpose, exploration by optimization (ExO), was
proposed, which achieves the optimal bounds in adversarial environments with
follow-the-regularized-leader for a wide range of online decision-making
problems. However, a naive application of ExO in stochastic environments
significantly degrades regret bounds. To resolve this problem in locally
observable games, we first establish a novel framework and analysis for ExO
with a hybrid regularizer. This development allows us to significantly improve
the existing regret bounds of best-of-both-worlds (BOBW) algorithms, which
achieves nearly optimal bounds both in stochastic and adversarial environments.
In particular, we derive a stochastic regret bound of $O(\sum_{a \neq a^*} k^2
m^2 \log T / \Delta_a)$, where $k$, $m$, and $T$ are the numbers of actions,
observations and rounds, $a^*$ is an optimal action, and $\Delta_a$ is the
suboptimality gap for action $a$. This bound is roughly $\Theta(k^2 \log T)$
times smaller than existing BOBW bounds. In addition, for globally observable
games, we provide a new BOBW algorithm with the first $O(\log T)$ stochastic
bound.
