How Evolution May Work Through Curiosity-Driven Developmental Process

By Pierre-Yves Oudeyer, Linda B. Smith. 2016

175 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Social Psychology

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Citations in the corpus

Learning to Play with Intrinsically-Motivated Self-Aware Agents (2018, background): “One approach may be improving our formulation of curiosity from the simple adversarial concept to include additional notions of intrinsic motivation such as learning progress [33, 34, 38].”
A nice surprise? Predictive processing and the active pursuit of novelty (2018, background): “Related proposals have been made by (Oudeyer and Smith 2016) who suggest that the core information-theoretic principle should be one that drives organisms to experience ongoing improvements, manifesting as a continual decrease in prediction errors, rather than seeking simply to minimize prediction…”
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning (2018, unknown): “Pathak et al. (2017); Schmidhuber (2010)), or empowerment (e.”
CURIOUS: Intrinsically Motivated Multi-Task, Multi-Goal Reinforcement Learning (2018, background): “These figures demonstrate the existence of successive learning phases, that can be interpreted as developmental phases [3].”
World Discovery Models (2019, unknown): “…and machine learning (Oudeyer & Kaplan, 2008; Gottlieb et al., 2013; Schmidhuber, 1991a) that an agent maximising a simple intrinsic reward based on patterns that are both novel and learnable could exhibit essential aspects of intelligence such as autonomous development (Oudeyer & Smith, 2016).”
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning (2017, background): “An essential component of such organized spontaneous exploration is the intrinsic motivation system, also called curiosity-driven exploration system (Kidd and Hayden, 2015; Oudeyer and Smith, 2016; Gottlieb and Oudeyer, 2018).”
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (2018, unknown): “Although some modules might be easier to learn first, or necessary to learn others, individual experience can influence learning trajectories just as for humans (Oudeyer & Smith, 2016).”
What do we learn about development from baby robots? (2015, background): “’ Rather, what is needed is a shift to a systems perspective (see Blumberg, Develop ment evolving: the origins and meanings of instinct, WIREs Cogn Sci, and Lickliter, Developmental FIGURE 4 | Curiosity-driven learning and self-organization of developmental structure in the Playground Experiment.(26”