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Found 2 papers, 1 papers with code
A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models
We use these estimated Q-functions, along with a clustering algorithm, to select a subset of states that are the most pivotal for driving changes in Q-functions.
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions
We name our method PQR, as it sequentially estimates the Policy, the $Q$-function, and the Reward function by deep learning.
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