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Found 3 papers, 2 papers with code
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules
The proposed model consists of an encoder which is a neural structure responsible for learning informative features from the input sequence, and a decoder which is a DRL model responsible for learning profitable strategies based on the features extracted by the encoder.
Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning
The effect of different input representations on the performance of the models is investigated and the performance of DRL-based models in different markets and asset situations is studied.
A Deep Decoder Structure Based on WordEmbedding Regression for An Encoder-Decoder Based Model for Image Captioning
The existing approaches are based on neural encoder-decoder structures equipped with the attention mechanism.
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