Fetching the paper…
Reading the bibliography…
Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate progress in machine learning.
Wang, R., Lehman, J., Clune, J., and Stanley, K. O · 1901
Earlier work this paper cites.
Dota 2 with large scale deep reinforcement learning
OpenAI, Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., Józefowicz, R., Gray, S., Olsson, C., Pachocki, J., Petrov, M., de Oliveira Pinto, H. P., Raiman, J., Salimans, T., Schlatter, J., Schneider, J., Sidor, S., Sutskever, I., Tang, J., Wolski, F., and Zhang, S · 1912
Earlier work this paper cites.
Some studies in machine learning using the game of checkers. ii—recent progress
Samuel, A. L · 1967
Earlier work this paper cites.
Learning to control an inverted pendulum using neural networks
Anderson, C. W · 1989
Earlier work this paper cites.
An approach to the synthesis of life
Ray, T. S · 1991
Earlier work this paper cites.
Measurement of evolutionary activity
Bedau, M · 1992
Earlier work this paper cites.
Incremental evolution of complex general behavior
Gomez, F. and Miikkulainen, R · 1997
Earlier work this paper cites.
Challenges in coevolutionary learning: Arms-race dynamics, open-endedness, and mediocre stable states
Ficici, S. and Pollack, J · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
Earlier work this paper cites.
An empirical analysis of collaboration methods in cooperative coevolutionary algorithms
Wiegand, R. P., Liles, W. C., and Jong, K. A. D · 2001
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
Stanley, K. O. and Miikkulainen, R · 2002
Earlier work this paper cites.
The evolutionary origin of complex features
Lenski, R. E., Ofria, C., Pennock, R. T., and Adami, C · 2003
Earlier work this paper cites.
Ideal evaluation from coevolution
de Jong, E. D. and Pollack, J. B · 2004
Earlier work this paper cites.
Pfeiffer – A distributed open-ended evolutionary system
Langdon, W. B · 2005
Earlier work this paper cites.
Compositional pattern producing networks: A novel abstraction of development
Stanley, K. O · 2007
Earlier work this paper cites.
The arrow of complexity hypothesis (abstract)
Bedau, M · 2008
Earlier work this paper cites.
Exploiting open-endedness to solve problems through the search for novelty
Lehman, J. and Stanley, K. O · 2008
Earlier work this paper cites.
Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
What is intrinsic motivation? a typology of computational approaches
Oudeyer, P.-Y. and Kaplan, F · 2009
Earlier work this paper cites.
Towards directed open-ended search by a novelty guided evolution strategy
Graening, L., Aulig, N., and Olhofer, M · 2010
Earlier work this paper cites.
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Schmidhuber, J · 2010
Earlier work this paper cites.
Search-based procedural content generation: A taxonomy and survey
Togelius, J., Yannakakis, G. N., Stanley, K. O., and Browne, C · 2011
Earlier work this paper cites.
Curriculum learning for motor skills
Karpathy, A. and Van De Panne, M · 2012
Cited alongside, same era.
Coevolutionary Principles , pp. 987–1033
Popovici, E., Bucci, A., Wiegand, R. P., and De Jong, E. D · 2012
Cited alongside, same era.
The arcade learning environment: An evaluation platform for general agents
Bellemare, M., Naddaf, Y., Veness, J., and Bowling, M · 2013
Cited alongside, same era.
POWERPLAY: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem
Schmidhuber, J · 2013
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Openai baselines
Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Sidor, S., Wu, Y., and Zhokhov, P · 2017
Later among the works it cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Later among the works it cites.
Reverse curriculum generation for reinforcement learning
Florensa, C., Held, D., Wulfmeier, M., Zhang, M., and Abbeel, P · 2017
Later among the works it cites.
Intrinsically motivated goal exploration processes with automatic curriculum learning
Forestier, S., Mollard, Y., and Oudeyer, P.-Y · 2017
Later among the works it cites.
Emergence of locomotion behaviours in rich environments
Heess, N., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S., Riedmiller, M., et al · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kingma, D. and Ba, J · 2014
Cited alongside, same era.
Identifying necessary conditions for open-ended evolution through the artificial life world of chromaria
Soros, L. and Stanley, K. O · 2014
Cited alongside, same era.
Natural evolution strategies
Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., and Schmidhuber, J · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
Cited alongside, same era.
Robots that can adapt like animals
Cully, A., Clune, J., Tarapore, D., and Mouret, J.-B · 2015
Cited alongside, same era.
Illuminating search spaces by mapping elites
Mouret, J. and Clune, J · 2015
Cited alongside, same era.
Why greatness cannot be planned
Stanley, K. O. and Lehman, J · 2015
Cited alongside, same era.
Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W. M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K., et al · 2017
Later among the works it cites.
Teacher-student curriculum learning
Matiisen, T., Oliver, A., Cohen, T., and Schulman, J · 2017
Later among the works it cites.
Evolution strategies as a scalable alternative to reinforcement learning
Salimans, T., Ho, J., Chen, X., Sidor, S., and Sutskever, I · 2017
Later among the works it cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Later among the works it cites.
Open-endedness: The last grand challenge you’ve never heard of
Stanley, K. O., Lehman, J., and Soros, L · 2017
Later among the works it cites.
Emergent complexity via multi-agent competition
Bansal, T., Pachocki, J., Sidor, S., Sutskever, I., and Mordatch, I · 2018
Later among the works it cites.
What’s holding artificial life back from open-ended evolution?
Dolson, E., Vostinar, A., and Ofria, C · 2018
Later among the works it cites.
Automatic goal generation for reinforcement learning agents
Florensa, C., Held, D., Geng, X., and Abbeel, P · 2018
Later among the works it cites.
Illuminating generalization in deep reinforcement learning through procedural level generation
Justesen, N., Torrado, R. R., Bontrager, P., Khalifa, A., Togelius, J., and Risi, S · 2018
Later among the works it cites.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
Later among the works it cites.
Alphastar: An evolutionary computation perspective
Arulkumaran, K., Cully, A., and Togelius, J · 2019
Later among the works it cites.
Open-ended learning in symmetric zero-sum games
Balduzzi, D., Garnelo, M., Bachrach, Y., Czarnecki, W., Pérolat, J., Jaderberg, M., and Graepel, T · 2019
Later among the works it cites.
Clune, J · 2019
Later among the works it cites.
Go-explore: a new approach for hard-exploration problems
Ecoffet, A., Huizinga, J., Lehman, J., Stanley, K. O., and Clune, J · 2019
Later among the works it cites.
Designing neural networks through neuroevolution
Stanley, K. O., Clune, J., Lehman, J., and Miikkulainen, R · 2019
Later among the works it cites.
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al · 2019
Later among the works it cites.
Zhi, J., Wang, R., Clune, J., and Stanley, K. O · 2020
Closest in time.