Approximate Dynamic Programming Using Bellman Residual Elimination and Gaussian Process Regression
This paper presents an approximate policy iteration algorithm for solving infinite-horizon, discounted Markov decision processes (MDPs) for which a model of the system is available. The algorithm is similar in spirit to Bellman residual minimization methods. However, by using Gaussian process regres...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/58878 https://orcid.org/0000-0001-8576-1930 |