Feature Reinforcement Learning: Part II. Structured MDPs
References
- Bertsekas, D. P., and Tsitsiklis, J. N. 1996. Neuro-Dynamic Programming. Belmont, MA: Athena Scientific.
- Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Springer.
- Boutilier, C.; Dean, T.; and Hanks, S. 1999. Decision-Theoretic Planning: Structural Assumptions and Computational Leverage. Journal of Artificial Intelligence Research 11:1–94.
- Chow, C. K., and Liu, C. N. 1968. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory IT-14(3):462–467.
- Dean, T., and Kanazawa, K. 1989. A Model for Reasoning about Persistence and Causation. Computational Intelligence 5(3):142–150.
- Friedman, N.; Geiger, D.; and Goldszmid, M. 1997. Bayesian Network Classifiers. Machine Learning 29(2):131–163.
- Gaglio, M. 2007. Universal Search. Scholarpedia 2(11):2575.
- Goertzel, B., and Pennachin, C., eds. 2007. Artificial General Intelligence. Springer.
- Grünwald, P. D. 2007. The Minimum Description Length Principle. Cambridge: The MIT Press.
- Guestrin, C.; Koller, D.; Parr, R.; and Venkataraman, S. 2003. Efficient Solution Algorithms for Factored MDPs. Journal of Artificial Intelligence Research (JAIR) 19:399–468.
- Hutter, M. 2003. Optimality of Universal Bayesian Prediction for General Loss and Alphabet. Journal of Machine Learning Research 4:971–1000.
- Hutter, M. 2005. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Berlin: Springer.
- Hutter, M. 2009a. Feature Dynamic Bayesian Networks. In Proc. 2nd Conf. on Artificial General Intelligence (AGI’09), volume 8, 67–73. Atlantis Press.
- Hutter, M. 2009b. Feature Reinforcement Learning: Part I: Unstructured MDPs. Journal of Artificial General Intelligence 1:3–24.
- Kaelbling, L. P.; Littman, M. L.; and Cassandra, A. R. 1998. Planning and Acting in Partially Observable Stochastic Domains. Artificial Intelligence 101:99–134.
- Kearns, M., and Koller, D. 1999. Efficient Reinforcement Learning in Factored MDPs. In Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI-99), 740–747. San Francisco: Morgan Kaufmann.
- Koller, D., and Parr, R. 1999. Computing Factored Value Functions for Policies in Structured MDPs,. In Proc. 16st International Joint Conf. on Artificial Intelligence (IJCAI’99), 1332–1339.
- Koller, D., and Parr, R. 2000. Policy Iteration for Factored MDPs. In Proc. 16th Conference on Uncertainty in Artificial Intelligence (UAI-00), 326–334. San Francisco, CA: Morgan Kaufmann.
- Legg, S., and Hutter, M. 2007. Universal Intelligence: A Definition of Machine Intelligence. Minds & Machines 17(4):391–444.
- Lewis, D. D. 1998. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. In Proc. 10th European Conference on Machine Learning (ECML’98), 4–15. Chemnitz, DE: Springer.
- Littman, M. L.; Sutton, R. S.; and Singh, S. P. 2001. Predictive Representations of State. In Advances in Neural Information Processing Systems, volume 14, 1555–1561. MIT Press.
- McCallum, A. K. 1996. Reinforcement Learning with Selective Perception and Hidden State. Ph.D. Dissertation, Department of Computer Science, University of Rochester.
- Puterman, M. L. 1994. Markov Decision Processes — Discrete Stochastic Dynamic Programming. New York, NY: Wiley.
- Ross, S.; Pineau, J.; Paquet, S.; and Chaib-draa, B. 2008. Online Planning Algorithms for POMDPs. Journal of Artificial Intelligence Research 2008(32):663–704.
- Russell, S. J., and Norvig, P. 2003. Artificial Intelligence. A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall, 2nd edition.
- Singh, S.; Littman, M.; Jong, N.; Pardoe, D.; and Stone, P. 2003. Learning Predictive State Representations. In Proc. 20th International Conference on Machine Learning (ICML’03), 712– 719.
- Singh, S. P.; James, M. R.; and Rudary, M. R. 2004. Predictive State Representations: A New Theory for Modeling Dynamical Systems. In Proc. 20th Conference in Uncertainty in Artificial Intelligence (UAI’04), 512–518. Banff, Canada: AUAI Press.
- Strehl, A. L.; Diuk, C.; and Littman, M. L. 2007. Efficient Structure Learning in Factored-State MDPs. In Proc. 27th AAAI Conference on Artificial Intelligence, 645–650. Vancouver, BC: AAAI Press.
- Sutton, R. S., and Barto, A. G. 2018. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 2nd edition.
- Szita, I., and Lörincz, A. 2008. The Many Faces of Optimism: a Unifying Approach. In Proc. 12th International Conference (ICML 2008), volume 307, 1048–1055.
DOI: https://doi.org/10.2478/jagi-2021-0003 | Journal eISSN: 1946-0163
Language: English
Page range: 71 - 86
Submitted on: Oct 21, 2020
Accepted on: Apr 6, 2021
Published on: Jun 14, 2021
Published by: Artificial General Intelligence Society
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year
Keywords:
Related subjects:
© 2021 Marcus Hutter, published by Artificial General Intelligence Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.