An object-oriented approach to reinforcement learning in an action game

Proceedings of the 7th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2011

Volumn , Issue , 2011, Pages 164-169

  Mohan, Shiwali ^a   Laird, John E ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING TO PLAY; OBJECT ORIENTED APPROACH;

ARTIFICIAL INTELLIGENCE; HUMAN COMPUTER INTERACTION;

REINFORCEMENT LEARNING;

EID: 84868294132     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Barto, A. G., and Mahadevan, S. 2003. Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems 13(4):341-379.
2. Dietterich, T. G. 2000. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. Journal of Artificial Intelligence Research 13(1):227-303.
3. Diuk, C.; Cohen, A.; and Littman, M. 2008. An object-oriented representation for efficient reinforcement learning. In Proceedings of the 25th International Conference on Machine learning, 240-247.
4. Driessens, K. 2001. Relational reinforcement learning. Multi-Agent Systems and Applications 271-280.
5. Guestrin, C.; Koller, D.; Gearhart, C.; and Kanodia, N. 2003. Generalizing plans to new environments in relational MDPs. In Proceedings of International Joint Conference on Artificial Intelligence.
6. John, B., and Vera, A. 1992. A GOMS analysis of a graphic machine-paced, highly interactive task. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 251-258.
7. Laird, J., and van Lent, M. 2001. Human-level AI's killer application: Interactive Computer Games. AI Magazine 22(2).
8. Marthi, B.; Russell, S.; Latham, D.; and Guestrin, C. 2005. Concurrent hierarchical reinforcement learning. In Proceedings of the National Conference on Artificial Intelligence, volume 20, 1652.
9. Ponsen, M.; Spronck, P.; and Tuyls, K. 2006. Hierarchical reinforcement learning with deictic representation in a computer game. In Proceedings of the 18th Belgium-Netherlands Conference on Artificial Intelligence, 251-258.
10. Rummery, G., and Niranjan, M. 1994. On-line Q-learning using Connectionist Systems. In Tech. Report CUED/F-INFENG/TR166. Cambridge University.
11. Shelton, C. R. 2001. Balancing Multiple Sources of Reward in Reinforcement Learning. In Advances in Neural Information Processing Systems, 1082-1088.
12. Spronck, P.; Ponsen, M.; Sprinkhuizen-Kuyper, I.; and Postma, E. 2006. Adaptive game AI with dynamic scripting. Machine Learning 63(3):217-248.
13. Sutton, R., and Barto, A. 1998. Introduction to Reinforcement Learning. MIT Press, 1st edition.
14. Tanner, B., and White, A. 2009. RL-Glue: Language-independent software for reinforcement-learning experiments. The Journal of Machine Learning Research 10:2133-2136.
15. Wintermute, S. 2010. Using Imagery to Simplify Perceptual Abstraction in Reinforcement Learning Agents. In In Proceedings of the the Twenty-Fourth AAAI Conference on Artificial Intelligence.