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Found 2 papers, 1 papers with code
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions.
Learning Abstract Models for Long-Horizon Exploration
In our approach, a manager maintains an abstract MDP over a subset of the abstract states, which grows monotonically through targeted exploration (possible due to the abstract MDP).
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