Single-loop actor-critic achieves the first Õ(ε^{-2}) sample complexity for ε-optimal policies under minimal irreducibility assumptions.
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Value mirror descent integrates mirror descent into value iteration for discounted MDPs, delivering near-optimal sample complexity of order |S||A|(1-γ)^{-3}ε^{-2} for general convex regularizers and bounded Bregman divergence between generated and optimal policies.
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Achieving $\epsilon^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions
Single-loop actor-critic achieves the first Õ(ε^{-2}) sample complexity for ε-optimal policies under minimal irreducibility assumptions.
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Value Mirror Descent for Reinforcement Learning
Value mirror descent integrates mirror descent into value iteration for discounted MDPs, delivering near-optimal sample complexity of order |S||A|(1-γ)^{-3}ε^{-2} for general convex regularizers and bounded Bregman divergence between generated and optimal policies.