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In contrast to other approaches to adapt basis functions for RL, our optimization criterion measures the actual performance of the computed policies in the task, using simulation from a representative set of initial states.
A further discussion of this mechanism and the role of public (health) policies in this task is beyond the scope of this article.
With the emergence of high speed networks and operating systems that have features such as preemptive migration, the importance of designing task assignment policies for assigning tasks in multiple server farms has increased.
Our first contribution is a novel deep reinforcement learning algorithm, advantage-based regret minimization (ARM), which learns robust policies in visual navigation tasks in the presence of partial observability.
Health policy emphasized the shift in tasks from treatment to public health services to strengthen the rural health system and fight chronic disease.
One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption.
Policies on task shifting must be considered in context.
In the interest of productivity, some organizations have a predetermined scheduling policy, requiring that tasks be completed in a particular order.
The argument fronted for no policy on task shifting in Uganda was that once a policy was formulated, its removal would be difficult in the future in case task shifting did not work as was being thought to.
We classify these policies in two classes: policies which assume that the task size is known a priori, and policies which assume that the task size is not known a priori (like TAGS, TAPTF and TAPTF-WC).
He takes the party to task for its "lack of credible policies" in the areas of globalization and education.
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