Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper studies dynamic regret minimization for goal-oriented reinforcement learning modeled by a non-stationary stochastic shortest path problem with changing cost and transition functions. They propose several algorithms, with corresponding upper bounds, and lower bounds for this setting.
strengths_and_weaknesses: The problem this paper studies is novel and of great significance for RL theory. And the theoretical results and analysis are sound and complete. Overall, this is a good paper for RL theory.
questions: Can the results be applied to more general settings, for example linear MDP?
limitations: The authors have adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper studies dynamic regret minimization for goal-oriented reinforcement learning modeled by a non-stationary stochastic shortest path problem with changing cost and transition functions.
strengths_and_weaknesses: The paper extends the results of [Wei et al., 2021], with dynamism in amount of changes of the cost and transition functions. 

The key weakness is that the model difference as compared to [Wei et al., 2021] is not explained, and the novelty as compared to [Wei et al., 2021] is not explained. 
questions: Please see above. 
limitations: Please see above.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper initiates the study of learning stochastic shortest path in the nonstationary tabular setting. Specifically, this paper assumes the cost function and the transition kernel vary with respect to the time. It considers the case where the magnitudes of the total change in the cost and transition functions are bounded. Under such a setting, this paper proves several results. First, a lower bound that depends on the magnitudes of the total change of the cost and transition functions. Then it proposed an algorithm that is nearly optimal. 

strengths_and_weaknesses: Originality: As far as I know, this paper is the first to consider the nonstationary SSP problem and gives a regret lower bound and regret upper bound. The main algorithm borrows ideas from some well-known existing RL algorithms, but also faces several unique challenges. Still I think t

Quality: The quality is high. The problem is well-defined, and the mathematics looks solid to me.

Clarity: The writing is good. Despite being a very technical paper, the main text is easy to follow. 

Significance: SSP has been a quite important problem in RL in these two years. This paper makes a solid contribution to this area by considering a new problem setting (non-stationary change in cost and transition with bounded magnitude) and gives both a lower and upper bounds. 

questions: I have one high level question: Do you think there is any fundamental difference between using finite-horizon approximation and using infinite-horizon-discounted approximation (e.g. [1] ) to solve SSP? 
It would be good if the authors can give some thoughts. 

[1]: Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret

limitations: No special concerns.
review_title: null

Meta-review: metareview: This paper studies dynamic regret minimization for goal-oriented reinforcement learning modeled by a non-stationary stochastic shortest path problem with changing cost and transition functions. This paper proposes several algorithms, with nearly matching upper bounds and lower bounds when Delta_c, and Delta_p are known, and a slightly suboptimal dynamic regret when they are not known. All reviewers are convinced by the contribution of this paper, and I recommend acceptance.
confidence: Certain
Decision: Accept