Joel Lehman
Title
Cited by
Cited by
Year
Abandoning objectives: Evolution through the search for novelty alone
J Lehman, KO Stanley
Evolutionary computation 19 (2), 189-223, 2011
8042011
Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning
FP Such, V Madhavan, E Conti, J Lehman, KO Stanley, J Clune
arXiv preprint arXiv:1712.06567, 2017
5222017
Exploiting open-endedness to solve problems through the search for novelty.
J Lehman, KO Stanley
ALIFE, 329-336, 2008
5202008
An intriguing failing of convolutional neural networks and the coordconv solution
R Liu, J Lehman, P Molino, FP Such, E Frank, A Sergeev, J Yosinski
arXiv preprint arXiv:1807.03247, 2018
4122018
Designing neural networks through neuroevolution
KO Stanley, J Clune, J Lehman, R Miikkulainen
Nature Machine Intelligence 1 (1), 24-35, 2019
3142019
Evolving a diversity of virtual creatures through novelty search and local competition
J Lehman, KO Stanley
Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011
3132011
Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents
E Conti, V Madhavan, FP Such, J Lehman, KO Stanley, J Clune
arXiv preprint arXiv:1712.06560, 2017
2162017
Go-explore: a new approach for hard-exploration problems
A Ecoffet, J Huizinga, J Lehman, KO Stanley, J Clune
arXiv preprint arXiv:1901.10995, 2019
2072019
A neuroevolution approach to general atari game playing
M Hausknecht, J Lehman, R Miikkulainen, P Stone
IEEE Transactions on Computational Intelligence and AI in Games 6 (4), 355-366, 2014
2012014
The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities
J Lehman, J Clune, D Misevic, C Adami, L Altenberg, J Beaulieu, ...
Artificial life 26 (2), 274-306, 2020
1642020
Revising the evolutionary computation abstraction: minimal criteria novelty search
J Lehman, KO Stanley
Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010
1282010
Efficiently evolving programs through the search for novelty
J Lehman, KO Stanley
Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010
1142010
Novelty search and the problem with objectives
J Lehman, KO Stanley
Genetic programming theory and practice IX, 37-56, 2011
1102011
Why greatness cannot be planned: The myth of the objective
KO Stanley, J Lehman
Springer, 2015
1092015
Paired open-ended trailblazer (poet): Endlessly generating increasingly complex and diverse learning environments and their solutions
R Wang, J Lehman, J Clune, KO Stanley
arXiv preprint arXiv:1901.01753, 2019
942019
Combining search-based procedural content generation and social gaming in the petalz video game
S Risi, J Lehman, DB D'Ambrosio, R Hall, KO Stanley
Eighth Artificial Intelligence and Interactive Digital Entertainment Conference, 2012
752012
Evolving policy geometry for scalable multiagent learning
DB D'Ambrosio, J Lehman, S Risi, KO Stanley
Proceedings of the 9th International Conference on Autonomous Agents and …, 2010
742010
Effective diversity maintenance in deceptive domains
J Lehman, KO Stanley, R Miikkulainen
Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013
722013
Safe mutations for deep and recurrent neural networks through output gradients
J Lehman, J Chen, J Clune, KO Stanley
Proceedings of the Genetic and Evolutionary Computation Conference, 117-124, 2018
652018
ES is more than just a traditional finite-difference approximator
J Lehman, J Chen, J Clune, KO Stanley
Proceedings of the Genetic and Evolutionary Computation Conference, 450-457, 2018
652018
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