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ion plays an important role in the generalisation of knowledge andskills and is key to sample efficient learning. In this work, we study jointtemporal and state abstraction in reinforcement learning, wheretemporally-extended actions in the form of options induce temporalabstractions, while aggregation of similar states with respect to abstractoptions induces state abstractions. Many existing abstraction schemes ignorethe interplay of state and temporal abstraction. Consequently, the consideredoption policies often cannot be directly transferred to new environments due tochanges in the state space and transition dynamics. To address this issue, wepropose a novel abstraction scheme building on successor features. Thisincludes an algorithm for transferring abstract options across differentenvironments and a state abstraction mechanism that allows us to performefficient planning with the transferred options.

Option Transfer and SMDP Abstraction with Successor Features