Reinforcement learning for spatial processes

18th World IMACS Congress and MODSIM 2009 - International Congress on Modelling and Simulation: Interfacing Modelling and Simulation with Mathematical and Computational Sciences, Proceedings

Volumn , Issue , 2009, Pages 755-761

  Forsell, N ^a,b   Garcia, F ^a,b   Sabbadin, R ^a,b  

^a SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

^b CEMAGREF   (France)

Author keywords

Markov decision processes (MDPs); Planning under risk and uncertainty; Reinforcement Learning (RL); Spatial processes

Indexed keywords

AGRICULTURAL ROBOTS; BEHAVIORAL RESEARCH; CONSERVATION; ENVIRONMENTAL MANAGEMENT; FORESTRY; GRAPH ALGORITHMS; GRAPH STRUCTURES; GRAPHIC METHODS; MARKOV PROCESSES; MULTI AGENT SYSTEMS; NATURAL RESOURCES MANAGEMENT; STOCHASTIC SYSTEMS; UNCERTAINTY ANALYSIS;

CONFLICTING OBJECTIVES; MANAGEMENT STRATEGIES; MARKOV DECISION PROCESSES; NEAR-OPTIMAL PERFORMANCE; PLANNING UNDER UNCERTAINTY; SIMULATION-BASED MODELS; STOCHASTIC ENVIRONMENT; STOCHASTIC TRANSITIONS;

REINFORCEMENT LEARNING;

EID: 85086267296     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Bertsekas, D.P. & Tsitsiklis, J.N. (1996). Neuro-dynamic programming. Athena Scientific.
2. Blennow, K. & Sallnäs, O. (2004). WINDA-a system of models for assessing the probability of wind damage to forest stands within a landscape. Ecological Modelling 175, 87-99.
3. Boutilier, C., Dearden, R. & Goldszmidt, M. (2000). Stochastic Dynamic Programming with Factored Representations. Artificial Intelligence 121(1), 49-107.
4. Claus, C. & Boutilier, C. (1998). The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems. In: Proceedings of AAAI/IAAI pp. 746-752.
5. Finney, M.A. (1994). Modeling the spread and behavior of prescribed natural fires. In: Proceedings of 12th Conference on Fire and Forest Meteorology pp. 138-143.
6. Forsell, N. & Sabbadin, R. (2006). Approximate Linear-Programming Algorithms for Graph-Based Markov Decision Processes. In: Proceedings of ECAI, Riva del Garda, Italy pp. 590-599.
7. Forsell, N., Wikström, P., Garcia, F., Sabbadin, R., Blennow, K. & Eriksson, L.O. (2009). Management of the risk of wind damage in forestry: a graph-based Markov decision process approach. Annals of operations research
8. Gardiner, B.A., Marshall, B., Achim, A., Belcher, R.E. & Wood, C.J. (2005). The stability of different silvicultural systems: a wind-tunnel investigation. Forestry 78(5), 471-484.
9. Guestrin, C., Koller, D., Parr, R. & Venkataraman, S. (2003). Efficient Solution Algorithms for Factored MDPs. Journal of Artificial Intelligence Research 19, 399-468.
10. Guestrin, C., Lagoudakis, M.G. & Parr, R. (2002). Coordinated Reinforcement Learning. In: Proceedings of ICML pp. 227-234.
11. Kok, J.R. & Vlassis, N.A. (2006). Collaborative Multiagent Reinforcement Learning by Payoff Propagation. Journal of Machine Learning Research(7), 1789-1828.
12. Meilby, H., Strange, N. & Thorsen, B.J. (2001). Optimal spatial harvest planning under risk of windthrow. Forest Ecology and Management 149, 15-31.
13. Peyrard, N. & Sabbadin, R. (2006). Mean Field Approximation of the Policy Iteration Algorithm for Graph-Based Markov Decision Processes. In: Proceedings of ECAI pp. 595-599.
14. Puterman, M.L. (1994). Markov Decision Processes. New York: John Wiley and Sons.
15. Schneider, J.G., Wong, W.K., Moore, A.W. & Riedmiller, M.A. (1999). Distributed Value Functions. In: Proceedings of ICML pp. 371-378.
16. Scilab-a free scientific software package, h.w.s.o., INRIA (2004).
17. Sutton, R.S. & Barto, A.G. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press