Hierarchical Reinforcement Learning (HRL)

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HRL is a promising approach to extend traditional Reinforcement Learning (RL) methods to solve more complex tasks.


HIerarchical Reinforcement learning with Off-policy correction (HIRO)

HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations, learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods.