The performance of policy gradient methods is sensitive to hyperparameter settings that must be tuned for any new application. Widely used grid search methods for tuning hyperparameters are sample inefficient and computationally expensive. More advanced methods like Population Based Training that learn optimal schedules for hyperparameters instead of fixed settings can yield better results, but are also sample inefficient and computationally expensive. In this paper, we propose Hyperparameter Optimisation on the Fly (HOOF), a gradient-free meta-learning algorithm that can automatically learn an optimal schedule for hyperparameters that affect the policy update directly through the gradient. The main idea is to use existing trajectories sampled by the policy gradient method to optimise a one-step improvement objective, yielding a sample and computationally efficient algorithm that is easy to implement. Our experimental results across multiple domains and algorithms show that using HOOF to learn these hyperparameter schedules leads to faster learning with improved performance.
By contrast, parallel search methods like grid search and random search run multiple training runs with different hyperparameter settings in parallel to reduce wall clock time, but require more parallel computational resources. These methods are easy to implement, and have been shown to perform well @cite_0 @cite_23 .
@cite_0 @cite_23
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Extract-Transform-Load (ETL) programs are used to load data into data warehouses (DWs). An ETL program must extract data from sources, apply different transformations to it, and use the DW to look up insert the data. It is both time consuming to develop and to run an ETL program. It is, however, typically the case that the ETL program can exploit both task parallelism and data parallelism to run faster. This, on the other hand, makes the development time longer as it is complex to create a parallel ETL program. To remedy this situation, we propose efficient ways to parallelize typical ETL tasks and we implement these new constructs in an ETL framework. The constructs are easy to apply and do only require few modifications to an ETL program to parallelize it. They support both task and data parallelism and give the programmer different possibilities to choose from. An experimental evaluation shows that by using a little more CPU time, the (wall-clock) time to run an ETL program can be greatly reduced. This paper argues that computational grids can be used for far more types of applications than just trivially parallel ones. Algorithmic optimizations like latency-hiding and exploiting locality can be used effectively to obtain high performance on grids, despite the relatively slow wide-area networks that connect the grid resources. Moreover, the bandwidth of wide-area networks increases rapidly, allowing even some applications that are extremely communication intensive to run on a grid, provided the underlying algorithms are latency-tolerant. We illustrate large-scale parallel computing on grids with three example applications that search large state spaces: transposition-driven search, retrograde analysis, and model checking. We present several performance results on a state-of-the-art computer science grid (DAS-3) with a dedicated optical network.