  We initiate the study of dynamic regret minimization for goal-oriented
reinforcement learning modeled by a non-stationary stochastic shortest path
problem with changing cost and transition functions. We start by establishing a
lower bound $\Omega((B_{\star} SAT_{\star}(\Delta_c +
B_{\star}^2\Delta_P))^{1/3}K^{2/3})$, where $B_{\star}$ is the maximum expected
cost of the optimal policy of any episode starting from any state, $T_{\star}$
is the maximum hitting time of the optimal policy of any episode starting from
the initial state, $SA$ is the number of state-action pairs, $\Delta_c$ and
$\Delta_P$ are the amount of changes of the cost and transition functions
respectively, and $K$ is the number of episodes. The different roles of
$\Delta_c$ and $\Delta_P$ in this lower bound inspire us to design algorithms
that estimate costs and transitions separately. Specifically, assuming the
knowledge of $\Delta_c$ and $\Delta_P$, we develop a simple but sub-optimal
algorithm and another more involved minimax optimal algorithm (up to
logarithmic terms). These algorithms combine the ideas of finite-horizon
approximation [Chen et al., 2022a], special Bernstein-style bonuses of the MVP
algorithm [Zhang et al., 2020], adaptive confidence widening [Wei and Luo,
2021], as well as some new techniques such as properly penalizing long-horizon
policies. Finally, when $\Delta_c$ and $\Delta_P$ are unknown, we develop a
variant of the MASTER algorithm [Wei and Luo, 2021] and integrate the
aforementioned ideas into it to achieve $\widetilde{O}(\min\{B_{\star}
S\sqrt{ALK},
(B_{\star}^2S^2AT_{\star}(\Delta_c+B_{\star}\Delta_P))^{1/3}K^{2/3}\})$ regret,
where $L$ is the unknown number of changes of the environment.
