17,563 research outputs found
Reinforcement Learning with Perturbed Rewards
Recent studies have shown that reinforcement learning (RL) models are
vulnerable in various noisy scenarios. For instance, the observed reward
channel is often subject to noise in practice (e.g., when rewards are collected
through sensors), and is therefore not credible. In addition, for applications
such as robotics, a deep reinforcement learning (DRL) algorithm can be
manipulated to produce arbitrary errors by receiving corrupted rewards. In this
paper, we consider noisy RL problems with perturbed rewards, which can be
approximated with a confusion matrix. We develop a robust RL framework that
enables agents to learn in noisy environments where only perturbed rewards are
observed. Our solution framework builds on existing RL/DRL algorithms and
firstly addresses the biased noisy reward setting without any assumptions on
the true distribution (e.g., zero-mean Gaussian noise as made in previous
works). The core ideas of our solution include estimating a reward confusion
matrix and defining a set of unbiased surrogate rewards. We prove the
convergence and sample complexity of our approach. Extensive experiments on
different DRL platforms show that trained policies based on our estimated
surrogate reward can achieve higher expected rewards, and converge faster than
existing baselines. For instance, the state-of-the-art PPO algorithm is able to
obtain 84.6% and 80.8% improvements on average score for five Atari games, with
error rates as 10% and 30% respectively.Comment: AAAI 2020 (Spotlight
Hardware acceleration of reaction-diffusion systems:a guide to optimisation of pattern formation algorithms using OpenACC
Reaction Diffusion Systems (RDS) have widespread applications in computational ecology, biology, computer graphics and the visual arts. For the former applications a major barrier to the development of effective simulation models is their computational complexity - it takes a great deal of processing power to simulate enough replicates such that reliable conclusions can be drawn. Optimizing the computation is thus highly desirable in order to obtain more results with less resources. Existing optimizations of RDS tend to be low-level and GPGPU based. Here we apply the higher-level OpenACC framework to two case studies: a simple RDS to learn the âworkingsâ of OpenACC and a more realistic and complex example. Our results show that simple parallelization directives and minimal data transfer can produce a useful performance improvement. The relative simplicity of porting OpenACC code between heterogeneous hardware is a key benefit to the scientific computing community in terms of speed-up and portability
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Mainstream machine-learning techniques such as deep learning and
probabilistic programming rely heavily on sampling from generally intractable
probability distributions. There is increasing interest in the potential
advantages of using quantum computing technologies as sampling engines to speed
up these tasks or to make them more effective. However, some pressing
challenges in state-of-the-art quantum annealers have to be overcome before we
can assess their actual performance. The sparse connectivity, resulting from
the local interaction between quantum bits in physical hardware
implementations, is considered the most severe limitation to the quality of
constructing powerful generative unsupervised machine-learning models. Here we
use embedding techniques to add redundancy to data sets, allowing us to
increase the modeling capacity of quantum annealers. We illustrate our findings
by training hardware-embedded graphical models on a binarized data set of
handwritten digits and two synthetic data sets in experiments with up to 940
quantum bits. Our model can be trained in quantum hardware without full
knowledge of the effective parameters specifying the corresponding quantum
Gibbs-like distribution; therefore, this approach avoids the need to infer the
effective temperature at each iteration, speeding up learning; it also
mitigates the effect of noise in the control parameters, making it robust to
deviations from the reference Gibbs distribution. Our approach demonstrates the
feasibility of using quantum annealers for implementing generative models, and
it provides a suitable framework for benchmarking these quantum technologies on
machine-learning-related tasks.Comment: 17 pages, 8 figures. Minor further revisions. As published in Phys.
Rev.
Towards a dynamic learning perspective of entrepreneurship
This conceptual paper introduces a dynamic learning perspective of entrepreneurship that builds upon existing 'dominant' theoretical approaches to understanding entrepreneurial activity. As many aspects of entrepreneurial learning remain poorly understood, this paper presents key conclusions from in-depth empirical work and synthesises a broad range of contributory adult, management and individual learning literatures to develop a robust and integrated conceptualisation of entrepreneurial learning. Three interrelated elements of entrepreneurial learning are proposed - dynamic temporal phases, interrelated processes and overarching characteristics. The paper concludes by demonstrating how a 'learning lens' can be applied to create further avenues for research in entrepreneurship from a learning perspectiv
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