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This paper introduces an automated skill acquisition framework inreinforcement learning which involves identifying a hierarchical description ofthe given task in terms of abstract states and extended actions betweenabstract states. Identifying such structures present in the task provides waysto simplify and speed up reinforcement learning algorithms. These structuresalso help to generalize such algorithms over multiple tasks without relearningpolicies from scratch. We use ideas from dynamical systems to find metastableregions in the state space and associate them with abstract states. Thespectral clustering algorithm PCCA+ is used to identify suitable abstractionsaligned to the underlying structure. Skills are defined in terms of thesequence of actions that lead to transitions between such abstract states. Theconnectivity information from PCCA+ is used to generate these skills oroptions. These skills are independent of the learning task and can beefficiently reused across a variety of tasks defined over the same model. Thisapproach works well even without the exact model of the environment by usingsample trajectories to construct an approximate estimate. We also present ourapproach to scaling the skill acquisition framework to complex tasks with largestate spaces for which we perform state aggregation using the representationlearned from an action conditional video prediction network and use the skillacquisition framework on the aggregated state space.

Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering