Beyond reward: The problem of knowledge and data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7207 LNAI, Issue , 2012, Pages 2-6

  Sutton, Richard S ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84864841464     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31951-8_2     Document Type: Conference Paper

References (24)

1. Baird, L.C.: Residual algorithms: Reinforcement learning with function approximation. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 30-37 (1995)
2. Degris, T., Modayil, J.: Scaling-up knowledge for a cognizant robot. In: Notes of the AAAI Spring Symposium on Designing Intelligent Robots: Reintegrating AI (2012)
3. Konidaris, G., Barto, A.G.: Building portable options: Skill transfer in reinforcement learning. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 895-900 (2007)
4. Koop, A.: Investigating Experience: Temporal Coherence and Empirical Knowledge Representation. MSc. thesis, University of Alberta (2007)
5. Maei, H.R.: Gradient Temporal-Difference Learning Algorithms. PhD. thesis, University of Alberta (2011)
6. Maei, H.R., Sutton, R.S.: GQ(+): A general gradient algorithm for temporal-difference prediction learning with eligibility traces. In: Proceedings of the Third Conference on Artificial General Intelligence (2010)
7. Maei, H.R., Szepesvári, C., Bhatnagar, S., Precup, D., Silver, D., Sutton, R.S.: Convergent temporal-difference learning with arbitrary smooth function approximation. In: Advances in Neural Information Processing Systems, vol. 22. MIT Press (2009)
8. Maei, H.R., Szepesvári, C., Bhatnagar, S., Sutton, R.S.: Toward off-policy learning control with function approximation. In: Proceedings of the 27th International Conference on Machine Learning (2010)
9. Mannor, S., Menache, I., Hoze, A., Klein, U.: Dynamic abstraction in reinforcement learning via clustering. In: Proceedings of the Twenty-First International Conference on Machine Learning (2004)
10. McGovern, A., Sutton, R.S.: Macro-actions in reinforcement learning: An empirical analysis. Technical Report 98-70, University of Massachusetts, Department of Computer Science (1998)
11. Modayil, J., White, A., Sutton, R.S.: Multi-timescale nexting in a reinforcement learning robot. In: Proceedings of the 2012 Conference on Simulation of Adaptive Behaviour (to appear, 2012)
12. Parr, R.: Hierarchical Control and Learning for Markov Decision Processes. PhD thesis, University of California at Berkeley (1998)
13. Precup, D.: Temporal Abstraction in Reinforcement Learning. PhD thesis, University of Massachusetts (2000)
14. Rafols, E.J.: Temporal Abstraction in Temporal-difference Networks. MSc. thesis, University of Alberta (2006)
15. Singh, S., Barto, A.G., Chentanez, N.: Intrinsically motivated reinforcement learning. In: Advances in Neural Information Processing Systems, vol. 17, pp. 1281-1288 (2005)
16. Stolle, M., Precup, D.: Learning Options in Reinforcement Learning. In: Koenig, S., Holte, R.C. (eds.) SARA 2002. LNCS (LNAI), vol. 2371, pp. 212-223. Springer, Heidelberg (2002)
17. Sutton, R.S.: "Verification" and "Verfication, the key to AI" (2001), http://richsutton.com/IncIdeas/Verification.html, http://richsutton.com/IncIdeas/KeytoAI.html
18. Sutton, R.S.: The grand challenge of predictive empirical abstract knowledge. In:Working Notes of the IJCAI 2009 Workshop on Grand Challenges for Reasoning from Experiences (2009)
19. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (1998)
20. Sutton, R.S., Maei, H.R., Precup, D., Bhatnagar, S., Silver, D., Szepesvári, C., Wiewiora, E.: Fast gradient-descent methods for temporal-difference learning with linear function approximation. In: Proceedings of the 26th International Conference on Machine Learning (2009)
21. Sutton, R.S., Modayil, J., Delp, M., Degris, T., Pilarski, P.M., White, A., Precup, D.: Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. In: Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems, AAMAS (2011)
22. Sutton, R.S., Precup, D., Singh, S.: Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence 112, 181-211 (1999)
23. Sutton, R.S., Szepesvári, C., Maei, H.R.: A convergent O(n) algorithm for off-policy temporal-difference learning with linear function approximation. In: Advances in Neural Information Processing Systems, vol. 21. MIT Press (2009)
24. Tsitsiklis, J.N., Van Roy, B.: An analysis of temporal-difference learning with function approximation. IEEE Transactions on Automatic Control 42, 674-690 (1997)