Algorithm configuration methods optimize the performance of a parameterized heuristic algorithm on a given distribution of problem instances. Recent work introduced an algorithm configuration procedure ("Structured Procrastination") that provably achieves near optimal performance with high probability and with

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper under review, Optimizing Energy Production Using Policy Search describes a policy search algorithm for optimizing the energy production in a hydroelectric power plant. First, the problem is specified with a model of the system, the goal and the constraints. Afterwards, a predictive state representation is introduced for the inflow process. Finally, a policy search algorithm based on a random local search is presented and evaluated on a dataset of a real power-plant.

We train a pair of models, a policy that proposes the new piece of code to write, and a value function that assesses the prospects of the code written so-far.

Search (PNS), are effective in playing and solving games. However, the understanding of their performance in industrial applications is still limited.

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.