Real-world experiments show that our method achieves a superior trade-off between grasping performance and time costs.

Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster.

Numerical results demonstrate significant regret reductions by our method, in comparison with several baselines in a range of multi-armed and linear bandit problems.

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