A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
Abstract
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state, in order to plan and to generalize better out-of-distribution. The agent's architecture uses a set representation and a bottleneck mechanism, forcing the number of entities to which the agent attends at each planning step to be small. In experiments, we investigate the bottleneck mechanism with sets of customized environments featuring different dynamics. We consistently observe that the design allows agents to learn to plan effectively, by attending to the relevant objects, leading to better out-of-distribution generalization.
Cite
Text
Zhao et al. "A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning." NeurIPS 2021 Workshops: DeepRL, 2021.Markdown
[Zhao et al. "A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning." NeurIPS 2021 Workshops: DeepRL, 2021.](https://mlanthology.org/neuripsw/2021/zhao2021neuripsw-consciousnessinspired/)BibTeX
@inproceedings{zhao2021neuripsw-consciousnessinspired,
title = {{A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning}},
author = {Zhao, Harry and Liu, Zhen and Luan, Sitao and Zhang, Shuyuan and Precup, Doina and Bengio, Yoshua},
booktitle = {NeurIPS 2021 Workshops: DeepRL},
year = {2021},
url = {https://mlanthology.org/neuripsw/2021/zhao2021neuripsw-consciousnessinspired/}
}