MDP Abstraction with Successor Features

Abstraction plays an important role for generalisation of knowledge and skills, and is key to sample efficient learning and planning. For many complex problems an abstract plan can be formed first, which is then instantiated by filling in the necessary low-level details. Often, such abstract plans generalize well to related new problems. We study abstraction in the context of reinforcement learning, in which agents may perform state or temporal abstractions. Temporal abstractions aka options represent temporally-extended actions in the form of option policies. However, typically acquired option policies cannot be directly transferred to new environments due to changes in the state space or transition dynamics. Furthermore, many existing state abstraction schemes ignore the correlation between state and temporal abstraction. In this work, we propose successor abstraction, a novel abstraction scheme building on successor features. This includes an algorithm for encoding and instantiation of abstract options across different environments, and a state abstraction mechanism based on the abstract options. Our successor abstraction allows us to learn abstract environment models with semantics that are transferable across different environments through encoding and instantiation of abstract options. Empirically, we achieve better transfer and improved performance on a set of benchmark tasks as compared to relevant state of the art baselines.