We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards.
1 code implementation • 20 Jun 2017 • Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis, Phil Blunsom
Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions.
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs.
Ranked #4 on Code Generation on Django