PDR-ANPG achieves last-iterate ε-optimality gap and ε constraint violation in CMDPs with sample complexity Õ(ε^{-2} min{ε^{-2}, ε_bias^{-1/3}}) for parameterized policies with transferred compatibility error ε_bias.
Policy mirror descent for reinforcement learning: Linear convergence, new sampling complexity , and generalized problem classes
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Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs
PDR-ANPG achieves last-iterate ε-optimality gap and ε constraint violation in CMDPs with sample complexity Õ(ε^{-2} min{ε^{-2}, ε_bias^{-1/3}}) for parameterized policies with transferred compatibility error ε_bias.