A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
Harry Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio
Abstract
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during planning. The agent uses a bottleneck mechanism over a set-based representation to force the number of entities to which the agent attends at each planning step to be small. In experiments, we investigate the bottleneck mechanism with several sets of customized environments featuring different challenges. We consistently observe that the design allows the planning agents to generalize their learned task-solving abilities in compatible unseen environments by attending to the relevant objects, leading to better out-of-distribution generalization performance.
BibTeX
@inproceedings{
zhao2021a,
title={A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning},
author={Harry Zhao and Zhen Liu and Sitao Luan and Shuyuan Zhang and Doina Precup and Yoshua Bengio},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=jh1lAmTMOJp}
}