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The human intrinsic desire to pursue knowledge, also known as curiosity, is considered essential in the process of skill acquisition.
Solving Rubik’s Cube with a Robot Hand
OpenAI; Akkaya, I.; Andrychowicz, M.; Chociej, M.; Litwin, M.; McGrew, B.; Petron, A.; Paino, A.; Plappert, M.; Powell, G.; Ribas, R.; Schneider, J.; Tezak, N.; Tworek, J.; Welinder, P.; Weng, L.; Yuan, Q.; Zaremba, W.; and Zhang, L. 2019 · 1910
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Efficient memory-based learning for robot control
Moore, A. 1990 · 1990
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Curious model-building control systems
Schmidhuber, J. 1991 · 1991
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Learning algorithms for classification: A comparison on handwritten digit recognition
LeCun, Y.; Jackel, L. D.; Bottou, L.; Cortes, C.; Denker, J. S.; Drucker, H.; Guyon, I.; Muller, U. A.; Sackinger, E.; Simard, P.; et al. 1995 · 1995
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Bayesian Neural Networks
Bishop, C. M. 1997 · 1997
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RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments
Raileanu, R.; and Rocktäschel, T. 2020 · 2002
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Agent57: Outperforming the Atari Human Benchmark
Badia, A. P.; Piot, B.; Kapturowski, S.; Sprechmann, P.; Vitvitskyi, A.; Guo, D.; and Blundell, C. 2020a · 2003
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Bayesian Surprise Attracts Human Attention
Itti, L.; and Baldi, P. 2006 · 2006
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Intrinsic Motivation Systems for Autonomous Mental Development
Oudeyer, P.; Kaplan, F.; and Hafner, V. V. 2007 · 2007
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Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes
Schmidhuber, J. 2009 · 2009
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Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning
Bai, C.; Liu, P.; Wang, Z.; Liu, K.; Wang, L.; and Zhao, Y. 2020 · 2010
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Mastering Atari with Discrete World Models
Hafner, D.; Lillicrap, T.; Norouzi, M.; and Ba, J. 2021 · 2010
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Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010)
Schmidhuber, J. 2010 · 2010
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Chapter 5 - Intrinsic Motivation and Positive Development
Larson, R. W.; and Rusk, N. 2011 · 2011
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Evaluating Agents without Rewards
Matusch, B.; Ba, J.; and Hafner, D. 2020 · 2012
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MuJoCo: A physics engine for model-based control
Todorov, E.; Erez, T.; and Tassa, Y. 2012 · 2012
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The Arcade Learning Environment: An Evaluation Platform for General Agents
Bellemare, M. G.; Naddaf, Y.; Veness, J.; and Bowling, M. 2013 · 2013
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Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
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Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; Petersen, S.; Beattie, C.; Sadik, A.; Antonoglou, I.; King, H.; Kumaran, D.; Wierstra, D.; Legg, S.; and Hassabis, D. 2015 · 2015
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Trust Region Policy Optimization
Schulman, J.; Levine, S.; Abbeel, P.; Jordan, M.; and Moritz, P. 2015 · 2015
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Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Watter, M.; Springenberg, J. T.; Boedecker, J.; and Riedmiller, M. 2015 · 2015
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Concrete Problems in AI Safety
Amodei, D.; Olah, C.; Steinhardt, J.; Christiano, P.; Schulman, J.; and Mané, D. 2016 · 2016
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Unifying Count-Based Exploration and Intrinsic Motivation
Bellemare, M. G.; Srinivasan, S.; Ostrovski, G.; Schaul, T.; Saxton, D.; and Munos, R. 2016 · 2016
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Faulty Reward Functions in the Wild
Clark, J.; and Amodei, D. 2016 · 2016
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VIME: Variational Information Maximizing Exploration
Rainbow: Combining Improvements in Deep Reinforcement Learning
Hessel, M.; Modayil, J.; van Hasselt, H.; Schaul, T.; Ostrovski, G.; Dabney, W.; Horgan, D.; Piot, B.; Azar, M. G.; and Silver, D. 2018 · 2018
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Randomized Prior Functions for Deep Reinforcement Learning
Osband, I.; Aslanides, J.; and Cassirer, A. 2018 · 2018
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Tassa, Y.; Doron, Y.; Muldal, A.; Erez, T.; Li, Y.; de Las Casas, D.; Budden, D.; Abdolmaleki, A.; Merel, J.; Lefrancq, A.; Lillicrap, T.; and Riedmiller, M. 2018 · 2018
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Perception-Driven Curiosity with Bayesian Surprise
Bucher, B.; Arapin, A.; Sekar, R.; Duan, F.; Badger, M.; Daniilidis, K.; and Rybkin, O. 2019 · 2019
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Exploration by random network distillation
Burda, Y.; Edwards, H.; Storkey, A. J.; and Klimov, O. 2019b · 2019
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Houthooft, R.; Chen, X.; Duan, Y.; Schulman, J.; De Turck, F.; and Abbeel, P. 2016 · 2016
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Intrinsic and Extrinsic Motivation , 1–4
Legault, L. 2016 · 2016
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Continuous control with deep reinforcement learning
Lillicrap, T. P.; Hunt, J. J.; Pritzel, A.; Heess, N.; Erez, T.; Tassa, Y.; Silver, D.; and Wierstra, D. 2016 · 2016
Cited alongside, same era.
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Achiam, J.; and Sastry, S. 2017 · 2017
Cited alongside, same era.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Higgins, I.; Matthey, L.; Pal, A.; Burgess, C.; Glorot, X.; Botvinick, M.; Mohamed, S.; and Lerchner, A. 2017 · 2017
Cited alongside, same era.
Count-Based Exploration with Neural Density Models
Ostrovski, G.; Bellemare, M. G.; van den Oord, A.; and Munos, R. 2017 · 2017
Cited alongside, same era.
Curiosity-Driven Exploration by Self-Supervised Prediction
Pathak, D.; Agrawal, P.; Efros, A. A.; and Darrell, T. 2017 · 2017
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Learning Latent Dynamics for Planning from Pixels
Hafner, D.; Lillicrap, T.; Fischer, I.; Villegas, R.; Ha, D.; Lee, H.; and Davidson, J. 2019 · 2019
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EMI: Exploration with Mutual Information
Kim, H.; Kim, J.; Jeong, Y.; Levine, S.; and Song, H. O. 2019 · 2019
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Self-Supervised Exploration via Disagreement
Pathak, D.; Gandhi, D.; and Gupta, A. 2019 · 2019
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Model-Based Active Exploration
Shyam, P.; Jaśkowski, W.; and Gomez, F. 2019 · 2019
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Never Give Up: Learning Directed Exploration Strategies
Badia, A. P.; Sprechmann, P.; Vitvitskyi, A.; Guo, Z. D.; Piot, B.; Kapturowski, S.; Tieleman, O.; Arjovsky, M.; Pritzel, A.; Bolt, A.; and Blundell, C. 2020b · 2020
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Dream to Control: Learning Behaviors by Latent Imagination
Hafner, D.; Lillicrap, T. P.; Ba, J.; and Norouzi, M. 2020 · 2020
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Specification gaming: the flip side of AI ingenuity
Krakovna, V.; et al. 2020 · 2020
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Implicit Generative Modeling for Efficient Exploration
Ratzlaff, N.; Bai, Q.; Fuxin, L.; and Xu, W. 2020 · 2020
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Planning to Explore via Self-Supervised World Models
Sekar, R.; Rybkin, O.; Daniilidis, K.; Abbeel, P.; Hafner, D.; and Pathak, D. 2020 · 2020
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Novelty Search in Representational Space for Sample Efficient Exploration
Tao, R. Y.; Francois-Lavet, V.; and Pineau, J. 2020 · 2020
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Reinforcement Learning with Prototypical Representations
Yarats, D.; Fergus, R.; Lazaric, A.; and Pinto, L. 2021 · 2020
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State Entropy Maximization with Random Encoders for Efficient Exploration
Seo, Y.; Chen, L.; Shin, J.; Lee, H.; Abbeel, P.; and Lee, K. 2021 · 2021
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A learning gap between neuroscience and reinforcement learning
Wauthier, S. T.; Mazzaglia, P.; Çatal, O.; Boom, C. D.; Verbelen, T.; and Dhoedt, B. 2021 · 2021
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