Corpus ID: 202712723

On the Convergence of Approximate and Regularized Policy Iteration Schemes

@article{Smirnova2019OnTC,
  title={On the Convergence of Approximate and Regularized Policy Iteration Schemes},
  author={E. Smirnova and Elvis Dohmatob},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.09621}
}

E. Smirnova, Elvis Dohmatob

Published 2019

Mathematics, Computer Science

ArXiv

Algorithms based on the entropy regularized framework, such as Soft Q-learning and Soft Actor-Critic, recently showed state-of-the-art performance on a number of challenging reinforcement learning (RL) tasks. The regularized formulation modifies the standard RL objective and thus, generally, converges to a policy different from the optimal greedy policy of the original RL problem. Practically, it is important to control the suboptimality of the regularized optimal policy. In this paper, we… CONTINUE READING

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