Fetching the paper…
Reading the bibliography…
General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environment.
Harutyunyan A, Dabney W, Borsa D, Heess N, Munos R and Precup D (2019) The termination critic · 1902
Earlier work this paper cites.
Schlegel M, Chung W, Graves D, Qian J and White M (2019) Importance resampling for off-policy prediction · 1906
Earlier work this paper cites.
Wang T, Bao X, Clavera I, Hoang J, Wen Y, Langlois E, Zhang S, Zhang G, Abbeel P and Ba J (2019) Benchmarking model-based reinforcement learning · 1907
Earlier work this paper cites.
Boston: Houghton Mifflin
Gibson JJ (1950) The Perception Of The Visual World · 1950
Earlier work this paper cites.
Allen and Unwin, London
Gibson JJ (1966) The Senses Considered as Perceptual Systems · 1966
Earlier work this paper cites.
Synthese 17(1): 162–172
Gibson JJ (1967) New reasons for realism · 1967
Earlier work this paper cites.
Perception 5: 437–59
Lee D (1976) A theory of visual control of braking based on information about time-to-collision · 1976
Earlier work this paper cites.
Houghton Mifflin
Gibson JJ (1979) The Ecological Approach to Visual Perception · 1979
Earlier work this paper cites.
In: Dietterich TG, Becker S and Ghahramani Z (eds.) Advances in Neural Information Processing Systems 14 . MIT Press, pp. 1555–1561
Littman ML and Sutton RS (2002) Predictive representations of state · 1983
Earlier work this paper cites.
Journal of experimental psychology. Human perception and performance 10: 683–703
Warren W (1984) Perceiving affordances: Visual guidance of stair climbing · 1984
Earlier work this paper cites.
Journal of experimental psychology. Human perception and performance 13 3: 361–70
Mark LS (1987) Eyeheight-scaled information about affordances: a study of sitting and stair climbing · 1987
Earlier work this paper cites.
New York: Basic Books
Norman D (1988) The Design of Everyday Things · 1988
Earlier work this paper cites.
Machine Learning 3(1): 9–44
Sutton RS (1988) Learning to predict by the methods of temporal differences · 1988
Earlier work this paper cites.
Journal of experimental psychology. Human perception and performance 18: 691–7
Konczak J, Meeuwsen H and Cress ME (1992) Changing affordances in stair climbing: The perception of maximum climbability in young and older adults · 1992
Earlier work this paper cites.
Ecological Psychology 4(3): 173–187
Turvey M (1992) Affordances and prospective control: An outline of the ontology · 1992
Earlier work this paper cites.
Philosopical Quarterly 44(175): 190–205
McDowell J (1994) The content of perceptual experience · 1994
Earlier work this paper cites.
Commun. ACM 38(3): 58–68
Tesauro G (1995) Temporal difference learning and td-gammon · 1995
Earlier work this paper cites.
MIT Press, A Bradford Book
Smith BC (1996) On the Origin of Objects · 1996
Earlier work this paper cites.
Adaptive Behavior 6(3-4): 473–507
Duchon AP, Kaelbling LP and Warren WH (1998) Ecological robotics · 1998
Earlier work this paper cites.
1st edition. Cambridge, MA, USA: MIT Press
Sutton RS and Barto AG (1998) Introduction to Reinforcement Learning · 1998
Earlier work this paper cites.
Artificial Intelligence 112(1): 181 – 211
Sutton RS, Precup D and Singh S (1999) Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning · 1999
Earlier work this paper cites.
Oxford University Press
Gibson E and Pick A (2000) An Ecological Approach To Perceptual Learning And Development · 2000
Earlier work this paper cites.
In: Proceedings of the Seventeenth International Conference on Machine Learning , ICML ’00. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc
Ng AY and Russell SJ (2000) Algorithms for inverse reinforcement learning · 2000
Earlier work this paper cites.
Psychological Science 13: 542 – 547
Gray R (2002) “markov at the bat”: A model of cognitive processing in baseball batters · 2002
Earlier work this paper cites.
Human movement science 22: 111–24
Cesari P, Formenti F and Olivato P (2003) A common perceptual parameter for stair climbing for children, young and old adults · 2003
Earlier work this paper cites.
Ecological Psychology 15(2): 181–195
Chemero A (2003) An outline of a theory of affordances · 2003
Earlier work this paper cites.
In: ICML ’04: Proceedings of the twenty-first international conference on Machine learning . New York, NY, USA: ACM
Abbeel P and Ng AY (2004) Apprenticeship learning via inverse reinforcement learning · 2004
Earlier work this paper cites.
London: Springer London
Corriou JP (2004) Model Predictive Control · 2004
Earlier work this paper cites.
MIT Press, A Bradford Book
Nöe A (2004) Action in Perception · 2004
Earlier work this paper cites.
In: Thrun S, Saul LK and Schölkopf B (eds.) Advances in Neural Information Processing Systems 16 . MIT Press, pp. 926–936
Toussaint M (2004) Learning a world model and planning with a self-organizing, dynamic neural system · 2004
Earlier work this paper cites.
In: Proceedings of the 19th International Joint Conference on Artificial Intelligence , IJCAI’05. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., pp. 835–840
Rafols EJ, Ring MB, Sutton RS and Tanner B (2005) Using predictive representations to improve generalization in reinforcement learning · 2005
Cited alongside, same era.
In: Proceedings of the 19th International Conference on Neural Information Processing Systems , NIPS’06. Cambridge, MA, USA: MIT Press, p. 1–8
Abbeel P, Coates A, Quigley M and Ng AY (2006) An application of reinforcement learning to aerobatic helicopter flight · 2006
Cited alongside, same era.
Infant Behavior and Development 30(1): 36 – 49
Berger SE, Theuring C and Adolph KE (2007) How and when infants learn to climb stairs · 2006
Cited alongside, same era.
Adaptive Behavior 15(4): 447–472
Şahin E, Çakmak M, Doğar MR, Uğur E and Üçoluk G (2007) To afford or not to afford: A new formalization of affordances toward affordance-based robot control · 2007
Cited alongside, same era.
Adaptive Behavior 15: 473–480
Chemero A and Turvey M (2007) Gibsonian affordances for roboticists · 2007
Cognitive Semiotics 9(1): 79–103
Jensen TW and Pedersen SB (2016) Affect and affordances: The role of action and emotion in social interaction · 2016
Later among the works it cites.
Nature 529: 484–503
Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T and Hassabis D (2016) Mastering the game of go with deep neural networks and tree search · 2016
Later among the works it cites.
Thomas V, Pondard J, Bengio E, Sarfati M, Beaudoin P, Meurs M, Pineau J, Precup D and Bengio Y (2017) Independently controllable factors · 2017
Later among the works it cites.
In: Proceedings of the 34th International Conference on Machine Learning , ICML 2017. Sydney, Australia
White M (2017) Unifying task specification in reinforcement learning · 2017
Later among the works it cites.
Advanced Robotics 31: 1086–1101
Yamanobe N, Wan W, Ramirez-Alpizar IG, Petit D, Tsuji T, Akizuki S, Hashimoto M, Nagata K and Harada K (2017) A brief review of affordance in robotic manipulation research · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Ghiassian S, Patterson A, Garg S, Gupta D, White A and White M (2020) Gradient temporal-difference learning with regularized corrections · 2007
Cited alongside, same era.
Penguin Publishing Group
Stadler M (2007) The Psychology of Baseball: Inside the Mental Game of the Major League Player · 2007
Cited alongside, same era.
In: Proceedings 2007 IEEE International Conference on Robotics and Automation . pp. 1721–1726
Ugur E, Dogar MR, Cakmak M and Sahin E (2007) The learning and use of traversability affordance using range images on a mobile robot · 2007
Cited alongside, same era.
Journal of Field Robotics Special Issue on the 2007 DARPA Urban Challenge, Part I 25(8): 425–466
Urmson C, Anhalt J, Bae H, Bagnell JAD, Baker CR, Bittner RE, Brown T, Clark MN, Darms M, Demitrish D, Dolan JM, Duggins D, Ferguson D, Galatali T, Geyer CM, Gittleman M, Harbaugh S, Hebert M, Howard T, Kolski S, Likhachev M, Litkouhi B, Kelly A, McNaughton M, Miller N, Nickolaou J, Peterson K, Pilnick B, Rajkumar R, Rybski P, Sadekar V, Salesky B, Seo YW, Singh S, Snider JM, Struble JC, Stentz AT, Taylor M, Whittaker WRL, Wolkowicki Z, Zhang W and Ziglar J (2008) Autonomous driving in urban environments: Boss and the urban challenge · 2007
Cited alongside, same era.
International journal of sport psychology 40: 79–107
Fajen B, Riley M and Turvey M (2008) Information, affordances, and the control of action in sport · 2008
Cited alongside, same era.
In: Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence , UAI’08. Arlington, Virginia, USA: AUAI Press
Sutton RS, Szepesvári C, Geramifard A and Bowling M (2008) Dyna-style planning with linear function approximation and prioritized sweeping · 2008
Cited alongside, same era.
In: 2009 24th International Symposium on Computer and Information Sciences . pp. 415–419
Ugur E, Sahin E and Oztop E (2009) Predicting future object states using learned affordances · 2009
Cited alongside, same era.
Later among the works it cites.
Adaptive Behavior 25(5): 235–271
Zech P, Haller S, Lakani SR, Ridge B, Ugur E and Piater J (2017) Computational models of affordance in robotics: a taxonomy and systematic classification · 2017
Later among the works it cites.
Achiam J, Edwards H, Amodei D and Abbeel P (2018) Variational option discovery algorithms · 2018
Later among the works it cites.
Eysenbach B, Gupta A, Ibarz J and Levine S (2018) Diversity is all you need: Learning skills without a reward function · 2018
Later among the works it cites.
PhD Thesis, Technische Universität München
Günther J (2018) Machine intelligence for adaptable closed loop and open loop production engineering systems · 2018
Later among the works it cites.
In: AAAI 2018 Fall Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy . pp. 22–29
Günther J, Kearney A, Dawson MR, Sherstan C and Pilarski PM (2018) Predictions, surprise, and predictions of surprise in general value function architectures · 2018
Later among the works it cites.
Hassanin M, Khan S and Tahtali M (2018) Visual affordance and function understanding: A survey · 2018
Later among the works it cites.
IEEE Transactions on Cognitive and Developmental Systems 10(1): 4–25
Jamone L, Ugur E, Cangelosi A, Fadiga L, Bernardino A, Piater J and Santos-Victor J (2018) Affordances in psychology, neuroscience, and robotics: A survey · 2018
Later among the works it cites.
Schlegel M, White A, Patterson A and White M (2018) General value function networks · 2018
Later among the works it cites.
Science 362(6419): 1140–1144
Silver D, Hubert T, Schrittwieser J, Antonoglou I, Lai M, Guez A, Lanctot M, Sifre L, Kumaran D, Graepel T, Lillicrap T, Simonyan K and Hassabis D (2018) A general reinforcement learning algorithm that masters chess, shogi, and go through self-play · 2018
Later among the works it cites.
MIT Press, A Bradford Book
Sutton RS and Barto AG (2018) Reinforcement Learning: An Introduction · 2018
Later among the works it cites.
Thomas V, Bengio E, Fedus W, Pondard J, Beaudoin P, Larochelle H, Pineau J, Precup D and Bengio Y (2018) Disentangling the independently controllable factors of variation by interacting with the world · 2018
Later among the works it cites.
In: 2019 IEEE International Conference on Intelligent Robots and Systems
Graves D, Rezaee K and Scheideman S (2019) Perception as prediction using general value functions in autonomous driving applications · 2019
Later among the works it cites.
Manuelli L, Gao W, Florence P and Tedrake R (2019) kpam: Keypoint affordances for category-level robotic manipulation
2019
Later among the works it cites.
arXiv preprint
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) Solving rubik’s cube with a robot hand · 2019
Later among the works it cites.
In: Elkind E, Veloso M, Agmon N and Taylor ME (eds.) Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS ’19, Montreal, QC, Canada, May 13-17, 2019 . International Foundation for Autonomous Agents and Multiagent Systems, pp. 332–340
Rafiee B, Ghiassian S, White A and Sutton RS (2019) Prediction in intelligence: An empirical comparison of off-policy algorithms on robots · 2019
Later among the works it cites.
MIT Press
Smith B (2019) The Promise of Artificial Intelligence: Reckoning and Judgment · 2019
Later among the works it cites.
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Sutton RS (2019) The bitter lesson · 2019
Later among the works it cites.
Nature 575
Vinyals O, Babuschkin I, Czarnecki W, Mathieu M, Dudzik A, Chung J, Choi D, Powell R, Ewalds T, Georgiev P, Oh J, Horgan D, Kroiss M, Danihelka I, Huang A, Sifre L, Cai T, Agapiou J, Jaderberg M and Silver D (2019) Grandmaster level in starcraft ii using multi-agent reinforcement learning · 2019
Later among the works it cites.
The International Journal of Robotics Research DOI: 10.1177/0278364919868017
Zeng A, Song S, Yu KT, Donlon E, Hogan FR, Bauza M, Ma D, Taylor O, Liu M, Romo E, Fazeli N, Alet F, Dafle NC, Holladay R, Morona I, Nair PQ, Green D, Taylor I, Liu W, Funkhouser T and Rodriguez A (2019) Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching · 2019
Later among the works it cites.
Journal of Neural Engineering 17(3): 036002
Dalrymple AN, Roszko DA, Sutton RS and Mushahwar VK (2020) Pavlovian control of intraspinal microstimulation to produce over-ground walking · 2020
Closest in time.
Frontiers in Robotics and AI 7: 34
Günther J, Ady NM, Kearney A, Dawson MR and Pilarski PM (2020) Examining the use of temporal-difference incremental delta-bar-delta for real-world predictive knowledge architectures · 2020
Closest in time.
In: 2020 IEEE International Conference on Robotics and Automation (ICRA) . pp. 6979–6985
Jin J, Nguyen NM, Sakib N, Graves D, Yao H and Jagersand M (2020) Mapless navigation among dynamics with social-safety-awareness: a reinforcement learning approach from 2d laser scans · 2020
Closest in time.
In: Proceedings of the 37th International Conference on Machine Learning , ICML 2020. Vienna, Austria
Khetarpal K, Ahmed Z, Comanici G, Abel D and Precup D (2020) What can i do here? a theory of affordances in reinforcement learning · 2020
Closest in time.
In: Proceedings of Robotics: Science and Systems (RSS)
Wu J, Sun X, Zeng A, Song S, Lee J, Rusinkiewicz S and Funkhouser T (2020) Spatial action maps for mobile manipulation · 2020
Closest in time.
In: Bessiere C (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 . International Joint Conferences on Artificial Intelligence Organization, pp. 3094–3100
Yang T, Hao J, Meng Z, Zhang Z, Hu Y, Chen Y, Fan C, Wang W, Liu W, Wang Z and Peng J (2020) Efficient deep reinforcement learning via adaptive policy transfer · 2020
Closest in time.