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Fuzzy critic-based learning forms a reinforcement learning method based on dynamic programming.
The algorithm is based on dynamic programming.
Optimal solutions based on dynamic programming are known.
The Needleman-Wunsch algorithm, which is based on dynamic programming, guarantees finding the optimal alignment of pairs of sequences.
Learning methods based on dynamic programming (DP) are receiving increasing attention in artificial intelligence.
A novel method which is based on dynamic programming approach is proposed.
An algorithm based on dynamic programming to solve this minimization problem is presented.
It provides a very accessible and interesting introduction to the literature on economic models based on dynamic programming methods that have been developed in the last several decades.
We then introduce a setup for the problem of safety verfication, and propose two approaches, both based on Dynamic Programming, to solve this problem.
Comparison with experimental data shows that our method estimates the TSE much more accurately than an existing method based on dynamic programming.
Although an optimal solution method based on dynamic programming is available, it is not practical for large DPLPs.
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