31,372 research outputs found
A hierarchical reinforcement learning method for persistent time-sensitive tasks
Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into developing methods to learn policies for complex persistent tasks and tasks that are time-sensitive. In this paper, we take a step towards solving this problem by using signal temporal logic (STL) as task specification, and taking advantage of the temporal abstraction feature that the options framework provide. We show via simulation that a relatively easy to implement algorithm that combines STL and options can learn a satisfactory policy with a small number of training cases
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks
Reinforcement learning has been applied to many interesting problems such as
the famous TD-gammon and the inverted helicopter flight. However, little effort
has been put into developing methods to learn policies for complex persistent
tasks and tasks that are time-sensitive. In this paper, we take a step towards
solving this problem by using signal temporal logic (STL) as task
specification, and taking advantage of the temporal abstraction feature that
the options framework provide. We show via simulation that a relatively easy to
implement algorithm that combines STL and options can learn a satisfactory
policy with a small number of training case
Havens: Explicit Reliable Memory Regions for HPC Applications
Supporting error resilience in future exascale-class supercomputing systems
is a critical challenge. Due to transistor scaling trends and increasing memory
density, scientific simulations are expected to experience more interruptions
caused by transient errors in the system memory. Existing hardware-based
detection and recovery techniques will be inadequate to manage the presence of
high memory fault rates.
In this paper we propose a partial memory protection scheme based on
region-based memory management. We define the concept of regions called havens
that provide fault protection for program objects. We provide reliability for
the regions through a software-based parity protection mechanism. Our approach
enables critical program objects to be placed in these havens. The fault
coverage provided by our approach is application agnostic, unlike
algorithm-based fault tolerance techniques.Comment: 2016 IEEE High Performance Extreme Computing Conference (HPEC '16),
September 2016, Waltham, MA, US
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