However, current systems focus on specific use-cases (e.g.

We will add additional discussion to the appendix.

This higher-level abstraction improves generalization in different prediction settings, but computing predictions often becomes intractable in large decision spaces. We propose the Soft-star algorithm, a softened heuristic-guided search technique for the maximum entropy inverse optimal control model of sequential behavior. This approach supports probabilistic search with bounded approximation error at a significantly reduced computational cost when compared to sampling based methods. We present the algorithm, analyze approximation guarantees, and compare performance with simulation-based inference on two distinct complex decision tasks.

This relaxed program is differentiable and can be trained end-to-end, and the resulting training loss is an approximately admissible heuristic that can guide the combinatorial search.

We thank the reviewer for the thorough review. We agree that our discussion of Seung et al. was not However, our contributions go beyond Seung et al.'s work in We kindly ask the reviewer to reconsider the following contributions. Such applications were not available in Seung et al. We indeed applied the results in Seung et al. as a tool to provide necessary conditions of convergence of the dynamics, Reviewer 2: We thank the reviewer for the enthusiastic support! We will provide details in the appendix. Minimax objectives: We thank the author for the inspiring question.

Then, we have that 1. y Fact 2. Let z:= [ x; y ] and z This can be easily proven using the AM-GM inequality. Fact 3. Let z:= [ x; y ] R It is a crucial building block for the algorithms in this work. The following classical theorem holds for AGD. We will start by giving a precise statement of Algorithm 1.Algorithm 1 Alternating Best Response (ABR)Require: g (,), Initial point z The basic idea is the following. The following two lemmas about the inexact APP A algorithm follow from the proof of Theorem 4.1 [ Here we provide their proofs for completeness.

Via reduction, our new bound also implies improved bounds for strongly convex-concave and convex-concave minimax optimization problems.

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