Entropy-based local fitnesses for evolutionary multiagent systems

Author:

Aydeniz Ayhan Alp1,Nickelson Anna1,Tumer Kagan1

Affiliation:

1. Oregon State University

Funder

Air Force Office of Scientific Research

National Science Foundation

Publisher

ACM

Reference10 articles.

1. Dave Cliff , Phil Husbands , and Inman Harvey . 1993. Explorations in evolutionary robotics. Adaptive behavior 2, 1 ( 1993 ), 73--110. Dave Cliff, Phil Husbands, and Inman Harvey. 1993. Explorations in evolutionary robotics. Adaptive behavior 2, 1 (1993), 73--110.

2. Benjamin Eysenbach , Abhishek Gupta , Julian Ibarz , and Sergey Levine . 2018. Diversity is all you need: Learning skills without a reward function. arXiv preprint arXiv:1802.06070 ( 2018 ). Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. 2018. Diversity is all you need: Learning skills without a reward function. arXiv preprint arXiv:1802.06070 (2018).

3. Atil Iscen , Ken Caluwaerts , Jonathan Bruce , Adrian Agogino , Vytas SunSpiral , and Kagan Tumer . 2015. Learning tensegrity locomotion using open-loop control signals and coevolutionary algorithms. Artificial life 21, 2 ( 2015 ), 119--140. Atil Iscen, Ken Caluwaerts, Jonathan Bruce, Adrian Agogino, Vytas SunSpiral, and Kagan Tumer. 2015. Learning tensegrity locomotion using open-loop control signals and coevolutionary algorithms. Artificial life 21, 2 (2015), 119--140.

4. Shauharda Khadka and Kagan Tumer . 2018 . Evolution-guided policy gradient in reinforcement learning . In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 1196--1208 . Shauharda Khadka and Kagan Tumer. 2018. Evolution-guided policy gradient in reinforcement learning. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 1196--1208.

5. Joel Lehman Kenneth O Stanley etal 2008. Exploiting open-endedness to solve problems through the search for novelty.. In ALIFE. Citeseer 329--336. Joel Lehman Kenneth O Stanley et al. 2008. Exploiting open-endedness to solve problems through the search for novelty.. In ALIFE. Citeseer 329--336.

Cited by 2 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. Entropy Maximization in High Dimensional Multiagent State Spaces;2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS);2023-12-04

2. Novelty Seeking Multiagent Evolutionary Reinforcement Learning;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12

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