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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.
Offline maximum entropy reinforcement learning seeks to estimate the optimal soft action-value function \(Q^*\) from a fixed dataset without further interaction with the environment. Although \(Q^*\) is characterized by the soft optimal Bellman equation, the corresponding Bellman operator cannot be evaluated directly b...
A weighted Bellman residual minimization framework that incorporates density ratio weighting by effectively integrating expert demonstrations with behavioral data is introduced, providing specific guidance for the rational utilization of expert demonstrations.
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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