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Distributional reinforcement learning for energy-based sequential models
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| Tetiana Parshakova, Marc Dymetman, Jean-Marc Andreoli |
| Workshop on the Optimization Foundations of Reinforcement Learning (OPTRL) at the Conference on Neural Information Processing Systems (NeurIPS), Vancouver, British Columbia, Canada, 8-14 December, 2019 |
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@article{parshakova2019distributional,
title={Distributional Reinforcement Learning for Energy-Based Sequential Models},
author={Parshakova, Tetiana and Andreoli, Jean-Marc and Dymetman, Marc},
journal={arXiv preprint arXiv:1912.08517},
year={2019}
}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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The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
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