Neural Information Processing Systems http://nips.cc/

Program translation isanimportant tool tomigrate legacycode inone language into an ecosystem built in a different language. In this work, we are the first to employ deep neural networks toward tackling this problem.

The task of sequential memory is considered challenging for models operating under biological constraints (i.e., local synaptic computations) for many reasons, including catastrophic forgetting,

Hence, with largestate and action spaces, it is essential to identify and exploit any possible structure existing in the system dynamics and reward function so as to minimize exploration phases and in turn reduce regret to reasonable values. Modern RL algorithms actually implicitly impose some structural properties either in the model parameters (transition probabilities and reward function, see e.g.

Reinforcement learning is well suited for optimizing policies of recommender systems.