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This work introduces an algorithm that identifies a small, relevant subset of features for each subgoal, yielding options that generalize broadly and accelerate exploration in three sparse-reward, image-based domains, including the Atari game MontezumasRevenge.
We study two-timescale decision systems in which a planning layer periodically supplies a continuation-value function to a real-time optimizer that allocates arriving resources, with inventory placement as our motivating application. We propose an end-to-end reinforcement learning (RL) method for learning this function...
This work aims to identify the benefits of HRL from the perspective of the fundamental challenges in decision-making, as well as highlight its impact on the performance trade-offs of AI agents.
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