A neurally plausible model learns successor representations in partially observable environments

1 code implementation • NeurIPS 2019 • Eszter Vertes, Maneesh Sahani

Animals need to devise strategies to maximize returns while interacting with their environment based on incoming noisy sensory observations.

reinforcement-learning Reinforcement Learning (RL)

Flexible and accurate inference and learning for deep generative models

no code implementations • NeurIPS 2018 • Eszter Vertes, Maneesh Sahani

We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process.