Information is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (2024)

Table of Contents
New Citation Alert added! New Citation Alert! NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems ABSTRACT References Cited By Recommendations Login options Full Access Published in Sponsors In-Cooperation Publisher Publication History Qualifiers Conference Funding Sources Other Metrics Article Metrics Other Metrics Cited By Digital Edition Caption Export Citations

Information is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (2)

Advanced Search

nips

research-article

Free Access

  • Authors:
  • Nicholas Rhinehart UC Berkeley

    UC Berkeley

    Search about this author

    ,
  • Jenny Wang UC Berkeley

    UC Berkeley

    Search about this author

    ,
  • Glen Berseth UC Berkeley

    UC Berkeley

    Search about this author

    ,
  • John D. Co-Reyes UC Berkeley

    UC Berkeley

    Search about this author

    ,
  • Danijar Hafner University of Toronto, Google Research, Brain Team

    University of Toronto, Google Research, Brain Team

    Search about this author

    ,
  • Chelsea Finn Stanford University

    Stanford University

    Search about this author

    ,
  • Sergey Levine UC Berkeley

    UC Berkeley

    Search about this author

NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing SystemsDecember 2021Article No.: 822Pages 10745–10758

Published:10 June 2024Publication History

  • 0citation
  • 0
  • Downloads

Metrics

Total Citations0Total Downloads0

Last 12 Months0

Last 6 weeks0

  • Get Citation Alerts

    New Citation Alert added!

    This alert has been successfully added and will be sent to:

    You will be notified whenever a record that you have chosen has been cited.

    To manage your alert preferences, click on the button below.

    Manage my Alerts

    New Citation Alert!

    Please log in to your account

  • Publisher Site

NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems

Information is power: intrinsic control via information capture

Pages 10745–10758

PreviousChapterNextChapter

Information is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (3)

ABSTRACT

Humans and animals explore their environment and acquire useful skills even in the absence of clear goals, exhibiting intrinsic motivation. The study of intrinsic motivation in artificial agents is concerned with the following question: what is a good general-purpose objective for an agent? We study this question in dynamic partially-observed environments, and argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model. This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states. We instantiate this approach as a deep reinforcement learning agent equipped with a deep variational Bayes filter. We find that our agent learns to discover, represent, and exercise control of dynamic objects in a variety of partially-observed environments sensed with visual observations without extrinsic reward.

References

  1. J. Achiam and S. Sastry. Surprise-based intrinsic motivation for deep reinforcement learning. arXiv preprint arXiv:1703.01732, 2017.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (4)
  2. E. H. Aoki, A. Bagchi, P. Mandal, and Y. Boers. A theoretical look at information-driven sensor management criteria. In 14th International Conference on Information Fusion, pages 1–8. IEEE, 2011.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (5)
  3. A. G. Barto, S. Singh, and N. Chentanez. Intrinsically motivated learning of hierarchical collections of skills. In Proceedings of the 3rd International Conference on Development and Learning, pages 112–19. Piscataway, NJ, 2004.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (6)
  4. M. Bellemare, S. Srinivasan, G. Ostrovski, T. Schaul, D. Saxton, and R. Munos. Unifying count-based exploration and intrinsic motivation. In Advances in Neural Information Processing Systems, pages 1471–1479, 2016.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (7)
  5. C. H. Bennett. The thermodynamics of computation—a review. International Journal of Theoretical Physics, 21(12):905–940, 1982.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (8)Cross Ref
  6. G. Berseth, D. Geng, C. M. Devin, N. Rhinehart, C. Finn, D. Jayaraman, and S. Levine. {SM}irl: Surprise minimizing reinforcement learning in unstable environments. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=cPZOyoDloxl.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (10)
  7. Y. Burda, H. Edwards, D. Pathak, A. Storkey, T. Darrell, and A. A. Efros. Large-Scale Study of Curiosity-Driven Learning. 2018. URL http://arxiv.org/abs/1808.04355.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (11)
  8. N. Chentanez, A. G. Barto, and S. P. Singh. Intrinsically motivated reinforcement learning. In Advances in neural information processing systems, pages 1281–1288, 2005.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (12)
  9. M. Chevalier-Boisvert. gym-miniworld environment for openai gym. https://github.com/maximecb/gym-miniworld, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (13)
  10. N. Das, M. Karl, P. Becker-Ehmck, and P. van der Smagt. Beta dvbf: Learning state-space models for control from high dimensional observations. arXiv preprint arXiv:1911.00756, 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (14)
  11. B. Eysenbach, A. Gupta, J. Ibarz, and S. Levine. Diversity is all you need: Learning skills without a reward function. arXiv preprint arXiv:1802.06070, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (15)
  12. H. J. S. Feder, J. J. Leonard, and C. M. Smith. Adaptive mobile robot navigation and mapping. The International Journal of Robotics Research, 18(7):650–668, 1999.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (16)Cross Ref
  13. C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine, and P. Abbeel. Deep spatial autoencoders for visuomotor learning. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 512–519. IEEE, 2016.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (18)
  14. Z. Fountas, N. Sajid, P. A. Mediano, and K. Friston. Deep active inference agents using monte-carlo methods. arXiv preprint arXiv:2006.04176, 2020.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (19)
  15. K. Friston. The free-energy principle: a rough guide to the brain? Trends in cognitive sciences, 13(7): 293–301, 2009.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (20)
  16. K. Friston. Life as we know it. Journal of the Royal Society Interface, 10(86):20130475, 2013.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (21)Cross Ref
  17. K. J. Friston, J. Daunizeau, and S. J. Kiebel. Reinforcement learning or active inference? PLOS ONE, 4 (7):1–13, 07 2009. doi: 10.1371/journal.pone.0006421. .Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (23)Cross Ref
  18. K. J. Friston, J. Daunizeau, J. Kilner, and S. J. Kiebel. Action and behavior: a free-energy formulation. Biological cybernetics, 102(3):227–260, 2010.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (25)Digital Library
  19. J. Fu, J. Co-Reyes, and S. Levine. Ex2: Exploration with exemplar models for deep reinforcement learning. In Advances in neural information processing systems, pages 2577–2587, 2017.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (27)
  20. M. Gheshlaghi Azar, B. Piot, B. Avila Pires, J.-B. Grill, F. Altché, and R. Munos. World discovery models. arXiv e-prints, pages arXiv–1902, 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (28)
  21. K. Gregor, D. J. Rezende, and D. Wierstra. Variational intrinsic control. arXiv preprint arXiv:1611.07507, 2016.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (29)
  22. D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson. Learning latent dynamics for planning from pixels. arXiv preprint arXiv:1811.04551, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (30)
  23. D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (31)
  24. D. Hafner, T. Lillicrap, M. Norouzi, and J. Ba. Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193, 2020.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (32)
  25. E. Hazan, S. Kakade, K. Singh, and A. Van Soest. Provably efficient maximum entropy exploration. In International Conference on Machine Learning, pages 2681–2691. PMLR, 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (33)
  26. R. Houthooft, X. Chen, Y. Duan, J. Schulman, F. De Turck, and P. Abbeel. VIME: Variational Information Maximizing Exploration. 2016. URL http://arxiv.org/abs/1605.09674.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (34)
  27. P. Izmailov, D. Podoprikhin, T. Garipov, D. Vetrov, and A. G. Wilson. Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (35)
  28. M. Karl, J. Bayer, and P. van der Smagt. Efficient empowerment. arXiv preprint arXiv:1509.08455, 2015.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (36)
  29. M. Karl, M. Soelch, J. Bayer, and P. van der Smagt. Deep variational bayes filters: Unsupervised learning of state space models from raw data. arXiv preprint arXiv:1605.06432, 2016.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (37)
  30. M. Karl, M. Soelch, P. Becker-Ehmck, D. Benbouzid, P. van der Smagt, and J. Bayer. Unsupervised real-time control through variational empowerment. arXiv preprint arXiv:1710.05101, 2017.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (38)
  31. M. Kempka, M. Wydmuch, G. Runc, J. Toczek, and W. Jaśkowski. Vizdoom: A doom-based ai research platform for visual reinforcement learning. In 2016 IEEE Conference on Computational Intelligence and Games (CIG), pages 1–8. IEEE, 2016.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (39)
  32. H. Kim, J. Kim, Y. Jeong, S. Levine, and H. O. Song. Emi: Exploration with mutual information. arXiv preprint arXiv:1810.01176, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (40)
  33. Y. Kim, W. Nam, H. Kim, J.-H. Kim, and G. Kim. Curiosity-bottleneck: Exploration by distilling task-specific novelty. In International Conference on Machine Learning, pages 3379–3388, 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (41)
  34. A. S. Klyubin, D. Polani, and C. L. Nehaniv. All else being equal be empowered. In M. S. Capcarrère, A. A. Freitas, P. J. Bentley, C. G. Johnson, and J. Timmis, editors, Advances in Artificial Life, pages 744–753, Berlin, Heidelberg, 2005. Springer Berlin Heidelberg. ISBN 978-3-540-31816-3.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (42)Digital Library
  35. A. S. Klyubin, D. Polani, and C. L. Nehaniv. Empowerment: A universal agent-centric measure of control. In 2005 IEEE Congress on Evolutionary Computation, volume 1, pages 128–135. IEEE, 2005.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (44)
  36. G. Konidaris and A. G. Barto. Skill discovery in continuous reinforcement learning domains using skill chaining. In Advances in neural information processing systems, pages 1015–1023, 2009.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (45)
  37. C. Kreucher, K. Kastella, and A. O. Hero Iii. Sensor management using an active sensing approach. Signal Processing, 85(3):607–624, 2005.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (46)Digital Library
  38. R. G. Krishnan, U. Shalit, and D. Sontag. Deep kalman filters. arXiv preprint arXiv:1511.05121, 2015.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (48)
  39. S. Lange and M. Riedmiller. Deep auto-encoder neural networks in reinforcement learning. In The 2010 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2010.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (49)
  40. A. X. Lee, A. Nagabandi, P. Abbeel, and S. Levine. Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model. arXiv preprint arXiv:1907.00953, 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (50)
  41. H. S. Leff and A. F. Rex. Maxwell's demon: entropy, information, computing. Princeton University Press, 2014.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (51)
  42. J. Lehman and K. O. Stanley. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary computation, 19(2):189–223, 2011.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (52)Digital Library
  43. M. Lopes, T. Lang, M. Toussaint, and P.-Y. Oudeyer. Exploration in model-based reinforcement learning by empirically estimating learning progress. In Advances in neural information processing systems, pages 206–214, 2012.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (54)
  44. C. J. Maddison, D. Lawson, G. Tucker, N. Heess, M. Norouzi, A. Mnih, A. Doucet, and Y. W. Teh. Filtering variational objectives. arXiv preprint arXiv:1705.09279, 2017.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (55)
  45. M. Magnasco. Szilard's heat engine. EPL (Europhysics Letters), 33(8):583, 1996.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (56)
  46. J. C. Maxwell and P. Pesic. Theory of heat. Courier Corporation, 2001.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (57)
  47. A. Mirchev, B. Kayalibay, M. Soelch, P. van der Smagt, and J. Bayer. Approximate bayesian inference in spatial environments. arXiv preprint arXiv:1805.07206, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (58)
  48. A. Mirchev, B. Kayalibay, P. van der Smagt, and J. Bayer. Variational state-space models for localisation and dense 3d mapping in 6 dof. arXiv preprint arXiv:2006.10178, 2020.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (59)
  49. S. Mohamed and D. Jimenez Rezende. Variational information maximisation for intrinsically motivated reinforcement learning. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2125–2133. Curran Associates, Inc., 2015.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (60)
  50. S. Mohamed and D. J. Rezende. Variational information maximisation for intrinsically motivated reinforcement learning. In Advances in neural information processing systems, pages 2125–2133, 2015.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (61)
  51. A. V. Nair, V. Pong, M. Dalal, S. Bahl, S. Lin, and S. Levine. Visual reinforcement learning with imagined goals. In Advances in Neural Information Processing Systems, pages 9191–9200, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (62)
  52. P.-Y. Oudeyer and F. Kaplan. What is intrinsic motivation? a typology of computational approaches. Frontiers in neurorobotics, 1:6, 2009.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (63)
  53. P.-Y. Oudeyer, F. Kaplan, and V. V. Hafner. Intrinsic motivation systems for autonomous mental development. IEEE transactions on evolutionary computation, 11(2):265–286, 2007.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (64)Digital Library
  54. T. Parr and K. J. Friston. Generalised free energy and active inference. Biological Cybernetics, 113(5): 495–513, Dec 2019. ISSN 1432-0770. doi: 10.1007/s00422-019-00805-w. .Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (66)Cross Ref
  55. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703, 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (68)
  56. D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell. Curiosity-driven Exploration by Self-supervised Prediction. 2017.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (69)Cross Ref
  57. D. Pathak, D. Gandhi, and A. Gupta. Self-Supervised Exploration via Disagreement. 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (71)
  58. R. Rafailov, T. Yu, A. Rajeswaran, and C. Finn. Offline reinforcement learning from images with latent space models. Learning for Decision Making and Control (L4DC), 2021.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (72)
  59. J. Schmidhuber. Curious model-building control systems. In Proc. international joint conference on neural networks, pages 1458–1463, 1991.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (73)Cross Ref
  60. J. Schmidhuber. Formal theory of creativity, fun, and intrinsic motivation (1990–2010). IEEE Transactions on Autonomous Mental Development, 2(3):230–247, 2010.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (75)Digital Library
  61. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (77)
  62. R. Sekar, O. Rybkin, K. Daniilidis, P. Abbeel, D. Hafner, and D. Pathak. Planning to explore via selfsupervised world models. In International Conference on Machine Learning, pages 8583–8592. PMLR, 2020.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (78)
  63. A. Sharma, S. Gu, S. Levine, V. Kumar, and K. Hausman. Dynamics-aware unsupervised discovery of skills. arXiv preprint arXiv:1907.01657, 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (79)
  64. P. Shyam, W. Jaśkowski, and F. Gomez. Model-based active exploration. arXiv preprint arXiv:1810.12162, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (80)
  65. B. C. Stadie, S. Levine, and P. Abbeel. Incentivizing exploration in reinforcement learning with deep predictive models. arXiv preprint arXiv:1507.00814, 2015.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (81)
  66. S. Still and D. Precup. An information-theoretic approach to curiosity-driven reinforcement learning. Theory in Biosciences, 131(3):139–148, 2012.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (82)Cross Ref
  67. Y. Sun, F. Gomez, and J. Schmidhuber. Planning to be surprised: Optimal bayesian exploration in dynamic environments. In International Conference on Artificial General Intelligence, pages 41–51. Springer, 2011.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (84)
  68. L. Szilard. Über die entropieverminderung in einem thermodynamischen system bei eingriffen intelligenter wesen. Zeitschrift für Physik, 53(11):840–856, 1929.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (85)Cross Ref
  69. H. Tang, R. Houthooft, D. Foote, A. Stooke, O. X. Chen, Y. Duan, J. Schulman, F. DeTurck, and P. Abbeel. # exploration: A study of count-based exploration for deep reinforcement learning. In Advances in neural information processing systems, pages 2753–2762, 2017.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (87)
  70. K. Ueltzhöffer. Deep active inference. Biological Cybernetics, 112(6):547–573, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (88)Digital Library
  71. G. Vezzani, A. Gupta, L. Natale, and P. Abbeel. Learning latent state representation for speeding up exploration. arXiv preprint arXiv:1905.12621, 2019.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (90)
  72. M. Watter, J. T. Springenberg, J. Boedecker, and M. Riedmiller. Embed to control: A locally linear latent dynamics model for control from raw images. arXiv preprint arXiv:1506.07365, 2015.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (91)
  73. G. Wayne, C.-C. Hung, D. Amos, M. Mirza, A. Ahuja, A. Grabska-Barwinska, J. Rae, P. Mirowski, J. Z. Leibo, A. Santoro, et al. Unsupervised predictive memory in a goal-directed agent. arXiv preprint arXiv:1803.10760, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (92)
  74. J. L. Williams. Information Theoretic Sensor Management. PhD thesis, Massachusetts Institute of Technology, 2007.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (93)Digital Library
  75. K. Xu, S. Verma, C. Finn, and S. Levine. Continual learning of control primitives: Skill discovery via reset-games. arXiv preprint arXiv:2011.05286, 2020.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (95)
  76. M. Zhang, S. Vikram, L. Smith, P. Abbeel, M. J. Johnson, and S. Levine. Solar: Deep structured latent representations for model-based reinforcement learning. arXiv preprint arXiv:1808.09105, 2018.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (96)
  77. R. Zhao, K. Lu, P. Abbeel, and S. Tiomkin. Efficient empowerment estimation for unsupervised stabilization. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=u2YNJPcQlwq.Google ScholarInformation is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (97)

Cited By

View all

Information is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (98)

    Recommendations

    • Extracting information from the power spectrum of voltage noise

      We outline an approximation for obtaining an analytic expression of the power spectral density (PSD) of the membrane potential (V"m) in neurons subject to synaptic noise. In high-conductance states, there is a remarkable agreement between this ...

      Read More

    • Glial cells for information routing?

      We investigate a possible functional role of glial cells as information routing devices of the cerebral cortex. On the one hand, functionally motivated models of neural information processing were lately suggested which rely on short-term changes of ...

      Read More

    • Self-enforcing coalitions with power accumulation

      Abstract

      Agents endowed with power compete for a divisible resource by forming coalitions with other agents. The coalition with the greatest power wins the resource and divides it among its members. The agents’ power increases according to their share of ...

      Read More

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    Get this Publication

    • Information
    • Contributors

    • Published in

      Information is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (99)

      NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems

      December 2021

      30517 pages

      ISBN:9781713845393

      • Editors:
      • M. Ranzato,
      • A. Beygelzimer,
      • Y. Dauphin,
      • P.S. Liang,
      • J. Wortman Vaughan

      Copyright © 2021 Neural Information Processing Systems Foundation, Inc.

      Sponsors

        In-Cooperation

          Publisher

          Curran Associates Inc.

          Red Hook, NY, United States

          Publication History

          • Published: 10 June 2024

          Qualifiers

          • research-article
          • Research
          • Refereed limited

          Conference

          Funding Sources

          • Information is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (100)

            Other Metrics

            View Article Metrics

          • Bibliometrics
          • Citations0

          • Article Metrics

            • Total Citations

              View Citations

            • Total Downloads

            • Downloads (Last 12 months)0
            • Downloads (Last 6 weeks)0

            Other Metrics

            View Author Metrics

          • Cited By

            This publication has not been cited yet

          Digital Edition

          View this article in digital edition.

          View Digital Edition

          • Figures
          • Other

            Close Figure Viewer

            Browse AllReturn

            Caption

            View Table of Contents

            Export Citations

              Your Search Results Download Request

              We are preparing your search results for download ...

              We will inform you here when the file is ready.

              Download now!

              Your Search Results Download Request

              Your file of search results citations is now ready.

              Download now!

              Your Search Results Download Request

              Your search export query has expired. Please try again.

              Information is power | Proceedings of the 35th International Conference on Neural Information Processing Systems (2024)
              Top Articles
              Funeral & Cremation Service in Goodyear, AZ - Simply Cremation
              Find the Best Funeral Home
              Best Pet Insurance That Pay The Vet Directly 🐕🐕🐕 (2024*)
              Best of the Money blog
              Storage Auctions: What You Need to Know - StorageTreasures Blog
              Best Selling Online Storage Auction Units of 2021 - StorageTreasures Blog
              Temple University 1098 Form
              How To Get 1098 T Form Grand Canyon University - Nationalparklore.com
              Latest Posts
              H-Frame Press | HyperPress® Series | HyperCyl
              Funeral Homes in Goodyear, Arizona
              Article information

              Author: Lidia Grady

              Last Updated:

              Views: 5635

              Rating: 4.4 / 5 (45 voted)

              Reviews: 92% of readers found this page helpful

              Author information

              Name: Lidia Grady

              Birthday: 1992-01-22

              Address: Suite 493 356 Dale Fall, New Wanda, RI 52485

              Phone: +29914464387516

              Job: Customer Engineer

              Hobby: Cryptography, Writing, Dowsing, Stand-up comedy, Calligraphy, Web surfing, Ghost hunting

              Introduction: My name is Lidia Grady, I am a thankful, fine, glamorous, lucky, lively, pleasant, shiny person who loves writing and wants to share my knowledge and understanding with you.