A novel log-barrier and log-determinant regularized algorithm achieves Õ(√T) regret in tabular MDPs with O(H log log T) oracle calls independent of |S|×|A| and extends to linear MDPs with infinite states for sublinear regret.
A provably efficient algorithm for linear markov decision process with low switching cost.arXiv preprint arXiv:2101.00494
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Model-Based Reinforcement Learning with Double Oracle Efficiency in Policy Optimization and Offline Estimation
A novel log-barrier and log-determinant regularized algorithm achieves Õ(√T) regret in tabular MDPs with O(H log log T) oracle calls independent of |S|×|A| and extends to linear MDPs with infinite states for sublinear regret.