Book Chapter10.1007/978-3-540-85563-7_2
Driven by Compression Progress
Jürgen Schmidhuber
- 03 Sep 2008
- pp 11-11
TL;DR: It is argued that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way.
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Abstract: I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way. Curiosity is the desire to create or discover more data that allows for compression progress. This drive motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and recent artificial systems.
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Citations
•Proceedings Article
Unifying count-based exploration and intrinsic motivation
Marc G. Bellemare,Sriram Srinivasan,Georg Ostrovski,Tom Schaul,David Saxton,Rémi Munos +5 more
- 05 Dec 2016
TL;DR: In this paper, the authors use density models to measure uncertainty and derive a pseudo-count from an arbitrary density model, which can be used to improve exploration in non-tabular reinforcement learning.
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010)
TL;DR: This overview first describes theoretically optimal (but not necessarily practical) ways of implementing the basic computational principles on exploratory, intrinsically motivated agents or robots, encouraging them to provoke event sequences exhibiting previously unknown, but learnable algorithmic regularities.
930
•Posted Content
Unifying Count-Based Exploration and Intrinsic Motivation
TL;DR: This work uses density models to measure uncertainty, and proposes a novel algorithm for deriving a pseudo-count from an arbitrary density model, which enables this technique to generalize count-based exploration algorithms to the non-tabular case.
180
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes
Jürgen Schmidhuber
- 18 Jun 2009
TL;DR: It is argued that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful.
Modeling effects of intrinsic and extrinsic rewards on the competition between striatal learning systems
TL;DR: This work extends a computational model of the dorsomedial and dorsolateral striatal reinforcement learning systems to account for the effects of extrinsic and intrinsic rewards and test the hypothesis that external rewards bias the competition in favor of the computationally efficient, but cruder and less flexible habitual system.
References
Introduction to Evolutionary Computing
Agoston E. Eiben,James C. Smith +1 more
- 01 Jan 2015
TL;DR: In the second edition, the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations as discussed by the authors.
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Curious model-building control systems
Jürgen Schmidhuber
- 18 Nov 1991
TL;DR: A novel curious model-building control system is described which actively tries to provoke situations for which it learned to expect to learn something about the environment, based on Watkins' Q-learning algorithm.
•Proceedings Article
A possibility for implementing curiosity and boredom in model-building neural controllers
Jürgen Schmidhuber
- 14 Feb 1991
TL;DR: It is described how the particular algorithm (as well as similar model-building algorithms) may be augmented by dynamic curiosity and boredom in a natural manner by introducing (delayed) reinforcement for actions that increase the model network's knowledge about the world.
607
Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts
TL;DR: It is pointed out how the fine arts can be formally understood as a consequence of the basic principle: given some subjective observer, great works of art and music yield observation histories exhibiting more novel, previously unknown compressibility/regularity/predictability than lesser works, thus deepening the observer’s understanding of the world and what is possible in it.
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