MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks

MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks

Menghui Zhu, Minghuan Liu, Jian Shen, Zhicheng Zhang, Sheng Chen, Weinan Zhang, Deheng Ye, Yong Yu, Qiang Fu, Wei Yang

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 3484-3491. https://doi.org/10.24963/ijcai.2021/480

In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem. In this paper, to enhance the diversity of relabeled goals, we develop FGI (Foresight Goal Inference), a new relabeling strategy that relabels the goals by looking into the future with a learned dynamics model. Besides, to improve sample efficiency, we propose to use the dynamics model to generate simulated trajectories for policy training. By integrating these two improvements, we introduce the MapGo framework (Model-Assisted Policy optimization for Goal-oriented tasks). In our experiments, we first show the effectiveness of the FGI strategy compared with the hindsight one, and then show that the MapGo framework achieves higher sample efficiency when compared to model-free baselines on a set of complicated tasks.
Keywords:
Machine Learning: Deep Reinforcement Learning
Machine Learning: Reinforcement Learning