Comparative studies of evolutionary methods and RL for learning behavior of virtual creatures-Reference-Cited by-同舟云学术

Comparative studies of evolutionary methods and RL for learning behavior of virtual creatures

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Symposium Series on Computational Intelligence (SSCI)
language:
Short-container-title:

Author:

Saito Takumi¹,Nishimura Haruki²,Oka Mizuki¹

Affiliation:

1. Graduate School of Science and Technology, University of Tsukuba,Tsukuba,Japan

2. College of Information Science, University of Tsukuba,Tsukuba,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10022049/10022014/10022282.pdf?arnumber=10022282

Reference15 articles.

1. Paired openended trailblazer (POET): endlessly generating increasingly complex and diverse learning environments and their solutions;wang;CoRR,2019

2. Proximal policy optimization algorithms;schulman;CoRR,2017

3. Quality Diversity: A New Frontier for Evolutionary Computation

4. Abandoning Objectives: Evolution Through the Search for Novelty Alone

5. A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lamarckian Co-design of Soft Robots via Transfer Learning;Proceedings of the Genetic and Evolutionary Computation Conference;2024-07-14

2. Exploring Embodied Intelligence in Soft Robotics: A Review;Biomimetics;2024-04-19

3. Evolving Complex Environments in Evolution Gym using Large Language Models;2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW);2024-04-14

4. Simulating Natural Selection with Deep Learning and Genetic Algorithm;Proceedings of the 5th International Conference on Information Management & Machine Intelligence;2023-11-23