Discover Life Skills for Planning with Bandits via Observing and
Learning How the World Works
- URL: http://arxiv.org/abs/2207.08130v1
- Date: Sun, 17 Jul 2022 10:05:54 GMT
- Title: Discover Life Skills for Planning with Bandits via Observing and
Learning How the World Works
- Authors: Tin Lai
- Abstract summary: We propose a novel approach for planning agents to compose abstract skills via observing and learning from historical interactions with the world.
Our framework operates in a Markov state-space model via a set of actions under unknown pre-conditions.
We show that this planning approach is experimentally very competitive in high-dimensional state space domains.
- Score: 3.0839245814393728
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a novel approach for planning agents to compose abstract skills
via observing and learning from historical interactions with the world. Our
framework operates in a Markov state-space model via a set of actions under
unknown pre-conditions. We formulate skills as high-level abstract policies
that propose action plans based on the current state. Each policy learns new
plans by observing the states' transitions while the agent interacts with the
world. Such an approach automatically learns new plans to achieve specific
intended effects, but the success of such plans is often dependent on the
states in which they are applicable. Therefore, we formulate the evaluation of
such plans as infinitely many multi-armed bandit problems, where we balance the
allocation of resources on evaluating the success probability of existing arms
and exploring new options. The result is a planner capable of automatically
learning robust high-level skills under a noisy environment; such skills
implicitly learn the action pre-condition without explicit knowledge. We show
that this planning approach is experimentally very competitive in
high-dimensional state space domains.
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