Sample based planning Sample Clauses

Sample based planning. If models are completely non-linear, local optimality may not be guaranteed and one must resort to sampling sequences. So called random shooting [B50] samples candidates from a fixed distribution and uses a model for evaluation to choose the best action. More advanced variants iteratively adjust the sampling distribution. For example, work from PETS [B39], combines a model-based approach together with an ensemble to forward sample trajectories. Each (dynamical) model encodes a probability distribution from which one may later sample. This algorithm was later used by [B38] to continuously control an adaptive optical mirror, whilst learning to adapt at the same time. In discrete environments, its more common to use search over tree-structures, than to iteratively refine a (single) trajectory. Monte Carlo Tree Search (MCTS) [B51] has formed to basis for many impressive results in game playing. Famously, AlphaGo [B52]
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  • General The Trustee shall keep proper books of record and account of all the transactions of each Trust under this Indenture at its corporate trust office, including a record of the name and address of, and the Units issued by each Trust and held by, every Unit holder, and such books and records of each Trust shall be open to inspection by any Unit holder of such Trust at all reasonable times during the usual business hours. The Trustee shall make such annual or other reports as may from time to time be required under any applicable state or federal statute or rule or regulations thereunder.

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