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A novel framework is proposed that decouples operator estimation from value function learning and establishes the smoothness of $Q^*$ through the Bellman equation using the the Holder assumptions on the conditional reward and transition densities.
In offline RL, estimating the optimal action-value function $Q^*$ can be formulated as solving the optimal Bellman equation based solely on offline observations. A fundamental challenge is that the reward function and transition kernel are unknown, so the optimal Bellman operator is not directly observable from data. T...
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