Distributional Reinforcement Learning for Energy-Based Sequential Models

Tetiana Parshakova; Jean-Marc Andreoli; Marc Dymetman

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arXiv · 1912.08517

Distributional Reinforcement Learning for Energy-Based Sequential Models

Tetiana Parshakova·Jean-Marc Andreoli·Marc Dymetman

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Abstract

Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al., CoNLL 2019] for exploiting global properties of sequences for data-efficient learning of seq2seq models. In the first phase of training, an Energy-Based model (EBM) over sequences is derived. This EBM has high representational power, but is unnormalized and cannot be directly exploited for sampling. To address this issue [Parshakova et al., CoNLL 2019] proposes a distillation technique, which can only be applied under limited conditions. By relating this problem to Policy Gradient techniques in RL, but in a \emph{distributional} rather than \emph{optimization} perspective, we propose a general approach applicable to any sequential EBM. Its effectiveness is illustrated on GAM-based experiments.

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Tetiana Parshakova, Jean-Marc Andreoli, Marc Dymetman. 2019-12-18. Distributional Reinforcement Learning for Energy-Based Sequential Models. https://arxiv.org/abs/1912.08517

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