FF+FPG: Guiding a Policy-Gradient planner

ICAPS 2007, 17th International Conference on Automated Planning and Scheduling

Volumn , Issue , 2007, Pages 42-48

  Buffet, Olivier ^a   Aberdeen, Douglas ^b  

^a UNIVERSITÉ DE TOULOUSE   (France)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

IMPORTANCE SAMPLING; STOCHASTIC SYSTEMS; TEACHING;

ABERDEEN; INTERNATIONAL PLANNING COMPETITIONS; LEARNING TIME; PLANNING DOMAINS; POLICY GRADIENT; PROBABILISTICS; RE-PLANNING; REINFORCEMENT LEARNINGS; STOCHASTIC LOCAL SEARCHES; TEACHERS';

REINFORCEMENT LEARNING;

EID: 57749179024     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Aberdeen, D., and Buffet, O. 2007. Temporal probabilistic planning with policy-gradients. In Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling (ICAPS'07).
2. Baxter, J.; Bartlett, P.; and Weaver, L. 2001. Experiments with infinite-horizon, policy-gradient estimation. Journal of Artificial Intelligence Research 15:351-381.
3. Buffet, O., and Aberdeen, D. 2006. The factored policy gradient planner (ipc'06 version). In Proceedings of the Fifth International Planning Competition (IPCS).
4. Fern, A.; Yoon, S.; and Givan, R. 2003. Approximate policy iteration with a policy language bias. In Advances in Neural Information Processing Systems 15 (NIPS'03).
5. Glynn, P., and Iglehart, D. 1989. Importance sampling for stochastic simulations. Management Science 35(11):1367-1392.
6. Hoffmann, J., and Nebel, B. 2001. The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14:253-302.
7. Hoffmann, J. 2001. FF: The fast-forward planning system. AI Magazine 22(3):57-62.
8. Little, I. 2006. Paragraph: A graphplan-based probabilistic planner. In Proceedings of the Fifth International Planning Competition (IPCS).
9. Mausam; Bertoli, P.; and Weld, D. S. 2007. A hybridized planner for stochastic domains. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI'07).
10. Meuleau, N.; Peshkin, L.; and Kim, K. 2001. Exploration in gradient-based reinforcement learning. Technical Report AI Memo 2001-003, MIT - AI lab.
11. Peshkin, L., and Shelton, C. 2002. Learning from scarce experience. In Proceedings of the Nineteenth International Conference on Machine Learning (ICML'02).
12. Rubinstein, R. 1981. Simulation and the Monte Carlo Method. John Wiley & Sons, Inc. New York, NY, USA.
13. Sanner, S., and Boutilier, C. 2006. Probabilistic planning via linear value-approximation of first-order MDPs. In Proceedings of the Fifth International Planning Competition (IPCS).
14. Shelton, C. 2001. Importance sampling for reinforcement learning with multiple objectives. Technical Report AI Memo 2001-003, MIT AI Lab.
15. Teichteil-Konigsbuch, F., and Fabiani, P. 2006. Symbolic stochastic focused dynamic programming with decision diagrams. In Proceedings of the Fifth International Planning Competition (IPCS).
16. Williams, R. 1992. Simple statistical gradient-following algorithms for connectionnist reinforcement learning. Machine Learning 8(3):229-256.
17. Xu, Y; Fern, A.; and Yoon, S. 2007. Discriminative learning of beam-search heuristics for planning. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI'07).
18. Yoon, S.; Fern, A.; and Givan, R. 2004. FF-rePlan. http://www.ecn.purdue. edu/sy/ffreplan.html.
19. Yoon, S.; Fern, A.; and Givan, B. 2007. FF-Replan: a baseline for probabilistic planning. In Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling (ICAPS'07).
20. Younes, H. L. S.; Liftman, M. L.; Weissman, D.; and Asmuth, J. 2005. The first probabilistic track of the international planning competition. Journal of Artificial Intelligence Research 24:851-887.