The options framework in reinforcement learning models the notion of a skill
or a temporally extended sequence of actions. The discovery of a reusable set
of skills has typically entailed building options, that navigate to bottleneck
states. This work adopts a complementary approach, where we attempt to discover
options that navigate to landmark states. These states are prototypical
representatives of well-connected regions and can hence access the associated
region with relative ease. In this work, we propose Successor Options, which
leverages Successor Representations to build a model of the state space. The
intra-option policies are learnt using a novel pseudo-reward and the model
scales to high-dimensional spaces easily. Additionally, we also propose an
Incremental Successor Options model that iterates between constructing
Successor Representations and building options, which is useful when robust
Successor Representations cannot be built solely from primitive actions. We
demonstrate the efficacy of our approach on a collection of grid-worlds, and on
the high-dimensional robotic control environment of Fetch.

探讨一种名为 Successor Options 的新方法，该方法使用 Successor Representations 构建状态空间模型，通过学习伪奖励来了解内部操作策略，并且适用于高维空间的机器人控制环境。