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Daniel Ratner

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4 papers

2 author rows

ICRA Conference 2020 Conference Paper

Reinforcement Learning for Adaptive Illumination with X-rays

Jean-Raymond Betterton
Daniel Ratner
Samuel Webb
Mykel J. Kochenderfer

We propose a learning algorithm for automating image sampling in scientific applications. We consider settings where images are sampled by controlling a probe beam's scanning trajectory over the image surface. We explore alternatives to obtaining images by the standard rastering method. We formulate the scanner control problem as a reinforcement learning (RL) problem and train a policy to adaptively sample only the highest value regions of the image, choosing the acquisition time and resolution for each sample position based on an observation of previous readings. We use convolutional neural network (CNN) policies to control the scanner as a way to generalize our approach to larger samples. We show simulation results for a simple policy on both synthetic data and real world data from an archaeological application.

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UAI Conference 2016 Conference Paper

Sparse Gaussian Processes for Bayesian Optimization

Mitchell McIntire
Daniel Ratner
Stefano Ermon

Bayesian optimization schemes often rely on Gaussian processes (GP). GP models are very flexible, but are known to scale poorly with the number of training points. While several efficient sparse GP models are known, they have limitations when applied in optimization settings. We propose a novel Bayesian optimization framework that uses sparse online Gaussian processes. We introduce a new updating scheme for the online GP that accounts for our preference during optimization for regions with better performance. We apply this method to optimize the performance of a free-electron laser, and demonstrate empirically that the weighted updating scheme leads to substantial improvements to performance in optimization.

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AAAI Conference 1986 Conference Paper

Finding a Shortest Solution for the N x N Extension of the 15-PUZZLE Is Intractable

Daniel Ratner

The 8-puzzle and the 15-puzzle have been used for many years as a domain for testing heuristic search techniques. From experience it is known that these puzzles are "difficult" and therefore useful for testing search techniques. In this paper we give strong evidence that these puzzles are indeed good test problems. We extend the 8-puzzle and the 15-puzzle to a nxn board and show that finding a shortest solution for the extended puzzle is NP-hard and thus computationally infeasible. We also present an approximation algorithm for transforming boards that is guaranteed to use no more than c*L(SP) moves, where L(SP) is the length of the shortest solution and c is a constant which is independent of the given boards and their size n .

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AAAI Conference 1986 Conference Paper

Joint and LPA*: Combination of Approximation and Search

Daniel Ratner

This paper describes two new algorithms, Joint and LPA*, which can be used to solve difficult combinatorial problems heuristically. The algorithms find reasonably short solution paths and are very fast. The algorithms work in polynomial time in the length of the solution. The algorithms have been benchmarked on the 15-puzzle, whose generalization has recently been shown to be NP hard, and outperform other known methods within this context.

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