no code implementations • 7 Sep 2022 • Zalán Borsos, Raphaël Marinier, Damien Vincent, Eugene Kharitonov, Olivier Pietquin, Matt Sharifi, Olivier Teboul, David Grangier, Marco Tagliasacchi, Neil Zeghidour
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency.
no code implementations • • Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem
In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL).
The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents.
While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.
One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning.