Shypd.aiThe largest open index of AI toolsRLSeq2Seq is an open-source deep reinforcement learning project for sequence-to-sequence models. It provides a TensorFlow-based framework for researchers and developers to implement and experiment with various RL methods for tasks like abstractive text summarization.
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RLSeq2Seq is an open-source project developed in TensorFlow, focusing on deep reinforcement learning (RL) for sequence-to-sequence (seq2seq) models. It addresses common problems in seq2seq models such as exposure bias and train/test inconsistency by integrating RL methods. The repository provides code for implementing various models, including Scheduled Sampling, Soft-Scheduled Sampling, End2EndBackProp, Policy-Gradient with Self-Critic learning, and Actor-Critic models using DDQN and Dueling networks. It is particularly geared towards abstractive text summarization, offering helper codes for processing datasets like CNN/Daily Mail and Newsroom. The project is suitable for researchers and developers looking to explore and apply advanced RL techniques to improve seq2seq model performance.
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