We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering. In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer. Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set. Our method makes learning faster, because it is highly parallelizable, and has better generalization performance than strong baselines, such as an ensemble of agents trained on the full data. We show that the improved performance is due to the increased diversity of reformulation strategies.
Multiple diverse query reformulation agents trained with reinforcement learning to improve search engines. Parellelization of ensemble method in reinforement learning for query reformulation, speeding up training and improving the diversity of learnt freformulations The authors propose to train multiple distinct agents, each over a different subset of the training set. The authors propose an ensemble approach for query reformulation