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Generating Music by Fine-Tuning Recurrent Neural Networks with Reinforcement Learning

Natasha Jaques
Shixiang Gu
Richard E. Turner
Deep Reinforcement Learning Workshop, NIPS (2016)

Abstract

Supervised learning with next-step prediction is a common way to train a sequence prediction model; however, it suffers from known failure modes and is notoriously difficult to train models to learn certain properties, such as having a coherent global structure. Reinforcement learning can be used to impose arbitrary properties on generated data by choosing appropriate reward functions. In this paper we propose a novel approach for sequence training, where we refine a sequence predictor by optimizing for some imposed reward functions, while maintaining good predictive properties learned from data. We propose efficient ways to solve this by augmenting deep Q-learning with a cross-entropy reward and deriving novel off-policy methods for RNNs from stochastic optimal control (SOC). We explore the usefulness of our approach in the context of music gener- ation. An LSTM is trained on a large corpus of songs to predict the next note in a musical sequence. This Note-RNN is then refined using RL, where the reward function is a combination of rewards based on rules of music theory, as well as the output of another trained Note-RNN. We show that this combination of ML and RL can not only produce more pleasing melodies, but that it can significantly reduce unwanted behaviors and failure modes of the RNN.

Research Areas