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We propose a novel approach for planning agents to compose abstract skillsvia observing and learning from historical interactions with the world. Ourframework operates in a Markov state-space model via a set of actions underunknown pre-conditions. We formulate skills as high-level abstract policiesthat propose action plans based on the current state. Each policy learns newplans by observing the states' transitions while the agent interacts with theworld. Such an approach automatically learns new plans to achieve specificintended effects, but the success of such plans is often dependent on thestates in which they are applicable. Therefore, we formulate the evaluation ofsuch plans as infinitely many multi-armed bandit problems, where we balance theallocation of resources on evaluating the success probability of existing armsand exploring new options. The result is a planner capable of automaticallylearning robust high-level skills under a noisy environment; such skillsimplicitly learn the action pre-condition without explicit knowledge. We showthat this planning approach is experimentally very competitive inhigh-dimensional state space domains.

Discover Life Skills for Planning with Bandits via Observing and Learning How the World Works