reviews: 
Comments: This paper considers the important problem of Inverse Reinforcement Learning for route optimization and planning, and specifically the practical limitations of existing methods when scaled to larger problems. The authors draw a unified theoretical perspective around Max Margin planning (Ratliff et al. 2006), the classic MaxEnt framework (Ziebart et al. 2008), and Bayesian IRL (Ramachandran and Amir 2007) which is helpful and insightful. Connections with graph theory methods lead to a novel IRL initialization and algorithm (MaxEnt++ and Receding Horizon Inverse Planning) which demonstrates significant improvements over other methods for large-scale problems. Several other graph optimization methods are presented which further allow scaling to global-scale routing problems.

The paper is well written, clear to read (despite covering a lot of theory and background), and the experimental evaluations are thorough and provide support for the claims.

I have a background in Inverse Reinforcement Learning theory, however have focused in other areas of computer science more recently, so may be out-of-touch with some recent literature results when performing this review. I have read the paper and appendices closely, however did not check the proofs carefully.
Strengths: * A compelling problem
 * Real-world empirical experimental problem considered
 * The paper does a good job straddling both novel theory advancements, and practical and engineering advancements, but presents the findings appropriately for the ICLR audience.
 * The connections with graph theoretic results (App. A1, A2, and Theorem B3) are useful and insightful.
 * The paper and appendices include negative results, in addition to the main results - this is encouraging to see (more papers should do this).
 * [Note to ACs and other reviewers]: Although the proposed method is framed for discrete MDP-based route optimization, note that there are several ways to generalize this framework to other interesting problem settings quite trivially, (see e.g. [A]) - as such, the findings here are actually quite broadly applicable, as noted by the authors in Sec 6.

# References

 * [A] Byravan, Arunkumar, et al. "Graph-Based Inverse Optimal Control for Robot Manipulation." IJCAI. Vol. 15. 2015.
Weaknesses: * The literature review is compact and the theory background provides a rapid but very nice summary of classical IRL results (in particular the unifying view of stochastic vs. deterministic policy trade-offs is helpful). One relevant piece of prior work that isn't mentioned however is the improved MaxEnt approach(es) by Snoswell et al. (e.g. [B, C]) - which address theoretical and empirical limitations with Ziebart's MaxEnt model, and are specifically applied to the problem of route optimization (albeit at city scale, not a global scale).

# References

 * [B] Snoswell, Aaron. "Modelling and explaining behaviour with Inverse Reinforcement Learning: Maximum Entropy and Multiple Intent methods." (2022).
 * [C] Snoswell, Aaron J., Surya PN Singh, and Nan Ye. "Revisiting maximum entropy inverse reinforcement learning: new perspectives and algorithms." 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 4 excellent
Clarity: 3 good