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A Genetic Search In Policy Space For Solving Markov Decision Processes
| Content Provider | Semantic Scholar |
|---|---|
| Author | Barash, Danny |
| Copyright Year | 1999 |
| Abstract | Markov Decision Processes (MDPs) have been studied extensively in the context of decision making under uncertainty. This paper presents a new methodology for solving MDPs, based on genetic algorithms. In particular, the importance of discounting in the new framework is dealt with and applied to a model problem. Comparison with the policy iteration algorithm from dynamic programming reveals the advantages and disadvantages of the proposed method. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.aaai.org/Papers/Symposia/Spring/1999/SS-99-07/SS99-07-002.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
