Predictive-State Decoders: Encoding the Future into Recurrent Networks

NeurIPS 2017 Arun VenkatramanNicholas RhinehartWen SunLerrel PintoMartial HebertByron BootsKris M. KitaniJ. Andrew Bagnell

Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement training loss. RNNs are used to model dynamic processes that are characterized by underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN... (read more)

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