Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods
Aditya Jaikumar Sharma, Zarana Parekh, Partha Talukdar · 2017
RLIE-DQN is a recently proposed Reinforcement Learning-based Information Extraction (IE)technique which is able to incorporate external evidence during the extraction process.RLIE-DQN trains a single agent sequentially, training on one instance at a time.This results in significant training slowdown which is undesirable.We leverage recent advances in parallel RL training using asynchronous methods and propose RLIE-A3C.RLIE-A3C trains multiple agents in parallel and is able to achieve upto 6x training speedup over RLIE-DQN, while suffering no loss in average accuracy.