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Boosting Interactive Evolution Using Human Computation Markets.

, and . TPNC, volume 8273 of Lecture Notes in Computer Science, page 1-18. Springer, (2013)

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Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents., , , , , and . CoRR, (2017)Analyzing deception, evolvability, and behavioral rarity in evolutionary robotics.. GECCO (Companion), page 1479-1486. ACM, (2017)First return then explore., , , , and . CoRR, (2020)An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents., , , , , , , , , and 1 other author(s). IJCAI, page 3260-3267. ijcai.org, (2019)Grasping novel objects with a dexterous robotic hand through neuroevolution., , , , and . CICA, page 125-132. IEEE, (2014)Quality Diversity through Human Feedback., , , , and . CoRR, (2023)Evolvability ES: Scalable and Direct Optimization of Evolvability., , , and . CoRR, (2019)Learning Belief Representations for Imitation Learning in POMDPs., , , and . UAI, volume 115 of Proceedings of Machine Learning Research, page 1061-1071. AUAI Press, (2019)Boosting Interactive Evolution Using Human Computation Markets., and . TPNC, volume 8273 of Lecture Notes in Computer Science, page 1-18. Springer, (2013)Reinforcement Learning Under Moral Uncertainty., and . ICML, volume 139 of Proceedings of Machine Learning Research, page 2926-2936. PMLR, (2021)