ClipUp: A Simple and Powerful Optimizer for Distribution-Based Policy Evolution

Toklu, Nihat Engin; Liskowski, Paweł; Srivastava, Rupesh Kumar

doi:10.1007/978-3-030-58115-2_36

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…III, and ReLU [61] is used as the activation function in between layers. For the algorithm module, we adopt PGPE [62] with ClipUp [63], and the population size is set to 300. On the problem module, we set the batch size to 3000.…”

Section: Acceleration Test Via Distributed Computingmentioning

confidence: 99%

EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation

Huang¹,

Cheng²,

Jin³

et al. 2023

Preprint

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During the past decades, evolutionary computation (EC) has demonstrated promising potential in solving various complex optimization problems of relatively small scales. Nowadays, however, ongoing developments in modern science and engineering are bringing increasingly grave challenges to the conventional EC paradigm in terms of scalability. As problem scales increase, on the one hand, the encoding spaces (i.e., dimensions of the decision vectors) are intrinsically larger; on the other hand, EC algorithms often require growing numbers of function evaluations (and probably larger population sizes as well) to work properly. To meet such emerging challenges, not only does it require delicate algorithm designs, but more importantly, a high-performance computing framework is indispensable. Hence, we develop a distributed GPU-accelerated algorithm library -EvoX. First, we propose a generalized workflow for implementing general EC algorithms. Second, we design a scalable computing framework for running EC algorithms on distributed GPU devices. Third, we provide user-friendly interfaces to both researchers and practitioners for benchmark studies as well as extended real-world applications. To comprehensively assess the performance of EvoX, we conduct a series of experiments, including: (i) scalability test via numerical optimization benchmarks with problem dimensions/population sizes up to millions; (ii) acceleration test via a neuroevolution task with multiple GPU nodes; (iii) extensibility demonstration via the application to reinforcement learning tasks on the OpenAI Gym. The code of EvoX is available at https://github.com/EMI-Group/EvoX.

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Section: Acceleration Test Via Distributed Computingmentioning

confidence: 99%

EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation

Huang¹,

Cheng²,

Jin³

et al. 2023

Preprint

View full text Add to dashboard Cite

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Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts

Tian¹,

Ha²

2022

Lecture Notes in Computer Science

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Computational creativity has contributed heavily to abstract art in modern era, allowing artists to create high quality, abstract two dimension (2D) arts with a high level of controllability and expressibility. However, even with computational approaches that have promising result in making concrete 3D art, computationally addressing abstract 3D art with highquality and controllability remains an open question. To fill this gap, we propose to explore computational creativity in making abstract 3D art by bridging evolution strategies (ES) and 3D rendering through customizable parameterization of scenes. We demonstrate that our approach is capable of placing semi-transparent triangles in 3D scenes that, when viewed from specified angles, render into films that look like artists' specification expressed in natural language. This provides a new way for the artist to easily express creativity ideas for abstract 3D art. The supplementary material, which contains code, animation for all figures, and more examples, is here: https://es3dart.github.io/. Figure 1: Our proposed method places semi-transparent triangles in three dimension (3D) spaces using Evolution Strategies (Tang, Tian, and Ha 2022; Hansen 2000; Hansen 2006). Leveraging ray-tracing based rendering Mitsuba 3 (Nimier-David et al. 2019; Jakob et al. 2022), the rendered film at possibly multiple cameras is compared with its corresponding, user-specified text prompt using distance between their representation embedded by CLIP (Radford et al. 2021). Such distances, aggregate by average, are used as the fitness in the sense of Evolution Strategies, which optimize the parameters of triangles to achieve better finesses.

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Exploring Spatial Cognition: Comparative Analysis of Agent-Based Models in Dynamic and Static Environments

Luongo,

Ponticorvo,

Milano

2024

Lecture Notes in Computer Science

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ClipUp: A Simple and Powerful Optimizer for Distribution-Based Policy Evolution

Cited by 6 publications

References 9 publications

EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation

EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation

Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts

Exploring Spatial Cognition: Comparative Analysis of Agent-Based Models in Dynamic and Static Environments

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