Optimizing time warp simulation with reinforcement learning techniques

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Optimizing time warp simulation with reinforcement learning techniques
Adaptive Time Warp protocols in the literature are usually based on a pre-defined analytic model of the system, expressed as a closed form function that maps system state to control parameter. The underlying assumption is that this model itself is optimal. In this paper we present a new approach that utilizes Reinforcement Learning techniques, also known as simulation-based dynamic programming. Instead of assuming an optimal control strategy, the very goal of Reinforcement Learning is to find the optimal strategy through simulation. A value function that captures the history of system feedbacks is used, and no prior knowledge of the system is required. Our reinforcement learning techniques were implemented in a distributed VLSI simulator with the objective of finding the optimal size of a bounded time window. Our experiments using two benchmark circuits indicated that it was successful in doing so.
Jun Wang, Carl Tropper
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2007
Where WSC
Authors Jun Wang, Carl Tropper
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