Finite-iteration guarantees are established for asynchronous scalar categorical TD in Cramér geometry and multivariate signed-categorical TD in MMD geometry under i.i.d., Markovian, and episodic sampling.
Risk-sensitive markov decision processes.Manage- ment science, 18(7):356–369
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DR-SAC is the first actor-critic distributionally robust RL algorithm for offline continuous control that derives a convergent robust soft policy iteration and reports up to 9.8x higher rewards than SAC under perturbations.
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A Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning
Finite-iteration guarantees are established for asynchronous scalar categorical TD in Cramér geometry and multivariate signed-categorical TD in MMD geometry under i.i.d., Markovian, and episodic sampling.
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DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
DR-SAC is the first actor-critic distributionally robust RL algorithm for offline continuous control that derives a convergent robust soft policy iteration and reports up to 9.8x higher rewards than SAC under perturbations.