Minimal criterion coevolution: a new approach to open-ended search

By Jonathan C. Brant, Kenneth O. Stanley. 2017

68 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Continual Learning of Control Primitives: Skill Discovery via Reset-Games (2020, methodology): “Unlike methods that adversarially vary the environment [6, 50], our method does not require any additional privileged capability to change the environment parameters.”
Learning quadrupedal locomotion over challenging terrain (2020, methodology): “The method uses minimal criteria (24, 40) and aims to choose environmental parameters that are neither too challenging nor trivial for the agents: This is realized by selecting task parameters that yield midrange rewards.”
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions (2019, methodology): “In a single run without any behavior characterization or archive (which are usually needed in QD algorithms), MCC can continually generate novel, diverse and increasingly complex mazes and maze solvers as new mazes provide new opportunities for innovation to the maze solvers (and vice versa), hintin”
Open-Ended Learning Leads to Generally Capable Agents (2021, positive): “An evolutionary algorithm governs the progression of the train worlds similarly to criterion co-evolution (Brant and Stanley, 2017).”
Open-ended Learning in Symmetric Zero-sum Games (2019, background): “There is a large literature on novelty search, open-ended evolution, and curiosity, which aim to continually expand the frontiers of game knowledge within an agent (Lehman & Stanley, 2008; Taylor et al., 2016; Banzhaf et al., 2016; Brant & Stanley, 2017; Pathak et al., 2017; Wang et al., 2019).”
AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence (2019, background): “The ﬁrst paper that made me consider directly optimizing environments as an interesting possibility is Brant and Stanley [17], where an expanding set of novel mazes were generated, each of which could be solved by at least one agent.”
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation (2018, background): “Similar ideas have been formulated within a coevolutionary framework as well [6].”
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions (2020, methodology): “…the ﬁeld of artiﬁcial life (Brant & Stanley, 2017; Graening et al., 2010; Langdon, 2005; Lehman & Stanley, 2008; Ray, 1991; Soros & Stanley, 2014; Soros et al., 2016; Standish, 2003b; Stanley et al., 2017; Taylor et al., 2016) has studied the prospects of open-ended computation for many years.”