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.

本文提出了一种名为 FGI 的新的重标记策略用于改善回报稀疏性问题，并通过引入动态模型来生成模拟轨迹来提高采样效率，提出了一种名为 MapGo 框架用于目标导向任务的模型辅助策略优化， 并在复杂任务上的实验证明了 FGI 策略相比后见策略的有效性，并且 MapGo 框架相对于无模型的基线表现出更高的采样效率。