A new primal-dual algorithm for adversarial linear CMDPs achieves the first sublinear regret and constraint violation bounds of order K to the 3/4 using weighted LogSumExp softmax policies with periodic mixing and regularized dual updates.
Conference on Learning Theory , pages=
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An actor-critic RL algorithm for low-rank MDPs achieves improved sample efficiency using solely a policy evaluation oracle.
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Primal-Dual Policy Optimization for Linear CMDPs with Adversarial Losses
A new primal-dual algorithm for adversarial linear CMDPs achieves the first sublinear regret and constraint violation bounds of order K to the 3/4 using weighted LogSumExp softmax policies with periodic mixing and regularized dual updates.
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Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs
An actor-critic RL algorithm for low-rank MDPs achieves improved sample efficiency using solely a policy evaluation oracle.