Dream to Control: Learning Behaviors by Latent Imagination
Abstract
Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.
Cite
Text
Hafner et al. "Dream to Control: Learning Behaviors by Latent Imagination." International Conference on Learning Representations, 2020.Markdown
[Hafner et al. "Dream to Control: Learning Behaviors by Latent Imagination." International Conference on Learning Representations, 2020.](https://mlanthology.org/iclr/2020/hafner2020iclr-dream/)BibTeX
@inproceedings{hafner2020iclr-dream,
title = {{Dream to Control: Learning Behaviors by Latent Imagination}},
author = {Hafner, Danijar and Lillicrap, Timothy and Ba, Jimmy and Norouzi, Mohammad},
booktitle = {International Conference on Learning Representations},
year = {2020},
url = {https://mlanthology.org/iclr/2020/hafner2020iclr-dream/}
}