PyBrain

By Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber. 2010

223 citations recorded by the source corpus.

Subfields: ML Systems & Hardware

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Citations in the corpus

OpenAI Gym (2016, background): “A variety of benchmarks have been released, such as the Arcade Learning Environment (ALE) [5], which exposed a collection of Atari 2600 games as reinforcement learning problems, and recently the RLLab benchmark for continuous control [6], to which we refer the reader for a survey on other RL benchma”
Dopamine: A Research Framework for Deep Reinforcement Learning (2018, background): “…algorithms across a variety of canonical tasks, and drove a yearly competition; BURLAP (MacGlashan, 2016) provides object-oriented support; PyBrain (Schaul et al., 2010) supports neural networks, and is perhaps closest to more recent frameworks; RLPy (Geramifard et al., 2015) is a lighter…”
Self-Delimiting Neural Networks (2012, background): “…through a focus on reducing search spaces by co-evolving the comparatively small weight vectors of individual neurons and synapses [19], by Natural Gradient-based Stochastic Search Strategies [80, 73, 74, 48, 17, 79], and by reducing search spaces through weight matrix compression [55, 35].”
RLPy: a value-function-based reinforcement learning framework for education and research (2015, background): “The need to unify and increase reusability of software packages for reinforcement learning research has been widely discussed (Tanner and White, 2009; Schaul et al., 2010), and many successful tools have been created (see Section 2).”
Exponential natural evolution strategies (2010, methodology): “B/2 · GB) end Table 1: Default parameter values for xNES as a function of problem dimension d. of xNES is available within the open-source machine learning library PyBrain [9].”
Pattern for Python (2012, background): “The source code is released under a BSD license, so it can be incorporated into proprietary products or used in combination with other open source packages such as SCRAPY (web mining), NLTK (natural language processing), PYBRAIN and PYML (machine learning) and NETWORKX (network analysis).”
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms (2012, methodology): “learning community has much aided such users by making available a wide variety of sophisticated learning algorithms and feature selection methods through open source packages, such as WEKA [14] and PyBrain [26].”
Machine learning for neuroimaging with scikit-learn (2014, background): “For instance, in Python, PyBrain (Schaul et al., 2010) is best at neural networks and reinforcement learning approaches, but its models are fairly black box, and do not match our need to interpret the results.”
Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network (2015, methodology): “A machine learning library written in Python called Pybrain [11] is appied in the implementation of the model.”
Selecting Near-Optimal Learners via Incremental Data Allocation (2015, background): “Moreover, the diverse set of learners available in today’s ML packages (Hall et al. 2009; Pedregosa and others 2011; Schaul et al. 2010; McCallum 2002) are continually expanding, and many of the most successful recent algorithms entail very heavy training costs (e.”
Modeling attacks on physical unclonable functions (2010, methodology): “We employed an in-house implementation of ES that is available from our machine learning library PyBrain [26].”
RLLib: C++ Library to Predict, Control, and Represent Learnable Knowledge Using On/Off Policy Reinforcement Learning (2015, background): “PyBrain(3)[19] and RLPy(4) are libraries written in Python to formulate and learn from RL problems.”
Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients (2010, methodology): “The implementations of the Capture Game, the algorithms and the Neural Network architectures are available in the open-source Machine Learning library PyBrain [19].”
PUF Modeling Attacks on Simulated and Silicon Data (2013, methodology): “We employed an in-house implementation of ES that is available from our machine learning library PyBrain [43].”
An Introduction to Deep Reinforcement Learning (2018, unknown): “• PyBrain (Schaul et al., 2010) is a machine learning library with some RL support.”
SkyAI: Highly modularized reinforcement learning library (2010, methodology): “config ={ Factor ={ resize(17, 5) [0]= ( 0, 0, 0, 0, 0) [1]= ( 1, 0, 0, 0, 0) [2]= ( 0, 0, 0, 0, 0) [3]= ( 0, 1, 0, 0, 0) [4]= ( 1, 0, 0, 0, 0) [5]= ( 0, 0, 0, 0, 0) [6]= ( 0, 1, 0, 0, 0) [7]= ( 0, 0, 0, 0, 0) [8]= ( 0, 0, 1, 0, 0) [9]= ( 0, 0, 0, 1, 0) [10]= ( 0, 0, 0, 0, 1) [11]= ( 0, 0, 0, 0, 0) ”
A video game description language for model-based or interactive learning (2013, methodology): “In our case, all the algorithms that do the actual learning1 are available in the PyBrain open source machine learning library [13].”
Exploring parameter space in reinforcement learning (2010, methodology): “The benchmarks and algorithms are included in the open source Machine Learning library PyBrain [20].”