论文标题
NLPGYM-用于评估自然语言处理任务的RL代理的工具包
NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing Tasks
论文作者
论文摘要
强化学习（RL）最近在复杂的游戏AI和机器人技术任务中表现出了令人印象深刻的表现。在很大程度上，这要归功于诸如OpenAI健身房，Atari学习环境或Malmo之类的模拟环境的可用性，这些环境可以通过与虚拟环境进行互动来学习复杂的任务。虽然RL也越来越多地应用于自然语言处理（NLP），但研究人员没有可用的模拟文本环境应用于NLP任务，并始终如一地基准为基准RL。因此，在此报告的工作中，我们发布了NLPGYM，这是一种开源Python工具包，为标准NLP任务（例如序列标签，多标签分类和问题答案）提供交互式文本环境。我们还使用不同的RL算法为6个任务提供了实验结果，这些算法是进一步研究的基准。该工具包发表在https://github.com/rajcscw/nlp-gym
Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks. To a large extent, this is thanks to the availability of simulated environments such as OpenAI Gym, Atari Learning Environment, or Malmo which allow agents to learn complex tasks through interaction with virtual environments. While RL is also increasingly applied to natural language processing (NLP), there are no simulated textual environments available for researchers to apply and consistently benchmark RL on NLP tasks. With the work reported here, we therefore release NLPGym, an open-source Python toolkit that provides interactive textual environments for standard NLP tasks such as sequence tagging, multi-label classification, and question answering. We also present experimental results for 6 tasks using different RL algorithms which serve as baselines for further research. The toolkit is published at https://github.com/rajcscw/nlp-gym
