arXiv Machine Learning
Tsallis Entropy Regularization for Linear Quadratic Regulator and Kullback-Leibler Control
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 17
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
arXiv:2310. 07211v2 Announce Type: replace Abstract: Regularization is a cornerstone of modern reinforcement learning.
arXiv Machine Learning
Sep 15
Learning to Solve Stochastic Controls with Unknown Drifts and Running Rewards: Theory, Algorithms and Convergence
The paper investigates continuous‑time stochastic control problems with unknown drift and running reward functions, using an exploratory reinforcement learning framework that incorporates relaxed controls and entropy regularization. It develops policy‑iteration algorithms based on probabilistic representations of the optimal value function and its gradient, proving convergence and demonstrating performance through numerical examples. The study also extends to a special case with control‑dependent diffusion, requiring a Hessian representation.
arXiv Machine Learning
Aug 10
Wasserstein Policy Gradient for Entropy-Regularized Linear-Quadratic Control
arXiv:2608. 07433v1 Announce Type: cross Abstract: Wasserstein policy gradient (WPG) updates state-conditional action laws by transport in the action space.
arXiv AI
Jun 2
Dynamic Entropy Tuning in Reinforcement Learning Low-Level Quadcopter Control: Stochasticity vs Determinism
arXiv:2512. 18336v2 Announce Type: replace-cross Abstract: This paper explores the impact of dynamic entropy tuning in Reinforcement Learning (RL) algorithms that train a stochastic policy.
arXiv Machine Learning
Jul 7
Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning
arXiv:2607. 03168v1 Announce Type: cross Abstract: Entropy regularization is widely used in continuous-time reinforcement learning (RL) to reduce sensitivity to environmental perturbations, yet its robustness benefits lack a rigorous theoretical foundation.
arXiv AI
6d ago
FERPO: Forward Entropy-Regularized Policy Optimization
arXiv:2610.02198v1 Announce Type: cross Abstract: Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic....