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Robert H. Klein

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2 papers

2 author rows

JMLR Journal 2015 Journal Article

RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research

Alborz Geramifard
Christoph Dann
Robert H. Klein
William Dabney
Jonathan P. How

RLPy is an object-oriented reinforcement learning software package with a focus on value-function-based methods using linear function approximation and discrete actions. The framework was designed for both educational and research purposes. It provides a rich library of fine-grained, easily exchangeable components for learning agents (e.g., policies or representations of value functions), facilitating recently increased specialization in reinforcement learning. RLPy is written in Python to allow fast prototyping, but is also suitable for large-scale experiments through its built-in support for optimized numerical libraries and parallelization. Code profiling, domain visualizations, and data analysis are integrated in a self-contained package available under the Modified BSD License at github.com/rlpy/rlpy. All of these properties allow users to compare various reinforcement learning algorithms with little effort. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2015. ( edit, beta )

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IROS Conference 2014 Conference Paper

Camera control for learning nonlinear target dynamics via Bayesian nonparametric Dirichlet-process Gaussian-process (DP-GP) models

Hongchuan Wei
Wenjie Lu 0005
Pingping Zhu
Silvia Ferrari
Robert H. Klein
Shayegan Omidshafiei
Jonathan P. How

This paper presents a camera control approach for learning unknown nonlinear target dynamics by approximating information value functions using particles that represent targets' position distributions. The target dynamics are described by a non-parametric mixture model that can learn a potentially infinite number of motion patterns. Assuming that each motion pattern can be represented as a velocity field, the target behaviors can be described by a non-parametric Dirichlet process-Gaussian process (DP-GP) mixture model. The DP-GP model has been successfully applied for clustering time-invariant spatial phenomena due to its flexibility to adapt to data complexity without overfitting. A new DP-GP information value function is presented that can be used by the sensor to explore and improve the DP-GP mixture model. The optimal camera control is computed to maximize this information value function online via a computationally efficient particle-based search method. The proposed approach is demonstrated through numerical simulations and hardware experiments in the RAVEN testbed at MIT.

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